library(ggeffects)
library(dplyr)

Attaching package: ‘dplyr’

The following objects are masked from ‘package:stats’:

    filter, lag

The following objects are masked from ‘package:base’:

    intersect, setdiff, setequal, union
library(ggpubr)
Loading required package: ggplot2
library(grid)
library(lmerTest)
Loading required package: lme4
Loading required package: Matrix
Registered S3 methods overwritten by 'lme4':
  method                          from
  cooks.distance.influence.merMod car 
  influence.merMod                car 
  dfbeta.influence.merMod         car 
  dfbetas.influence.merMod        car 

Attaching package: ‘lmerTest’

The following object is masked from ‘package:lme4’:

    lmer

The following object is masked from ‘package:stats’:

    step
library(devtools)
Loading required package: usethis
source_url("https://raw.githubusercontent.com/JacobElder/MiscellaneousR/master/corToOne.R")
SHA-1 hash of file is 07e3c11d2838efe15b1a6baf5ba2694da3f28cb1
source_url("https://raw.githubusercontent.com/JacobElder/MiscellaneousR/master/plotCommAxes.R")
SHA-1 hash of file is 374a4de7fec345d21628a52c0ed0e4f2c389df8e
fullLong1 <- read.csv("~/Google Drive/Volumes/Research Project/Identities to Traits/Study 1/Cleaning/Output/id2traitDf.csv", header = T)
orderDf1 <- read.csv( "~/Google Drive/Volumes/Research Project/Identities to Traits/Study 1/Cleaning/Output/orderDf.csv", header = T)
idShort1 <- read.csv( "~/Google Drive/Volumes/Research Project/Identities to Traits/Study 1/Cleaning/Output/id2traitShort.csv", header = T)
indDiff1 <- read.csv( "~/Google Drive/Volumes/Research Project/Identities to Traits/Study 1/Cleaning/Output/indDiff.csv", header = T)
idSim1 <- read.csv( "~/Google Drive/Volumes/Research Project/Identities to Traits/Study 1/Cleaning/Output/identitySimDf.csv", header = T)
#idToSim1 <- read.csv("~/Google Drive/Volumes/Research Project/Identities to Traits/Study 1/Cleaning/Output/simDf.csv", header = T)
fullLong2 <- read.csv("~/Google Drive/Volumes/Research Project/Identities to Traits/Study 2/Cleaning/Output/id2traitDf.csv", header = T)
orderDf2 <- read.csv( "~/Google Drive/Volumes/Research Project/Identities to Traits/Study 2/Cleaning/Output/orderDf.csv", header = T)
idShort2 <- read.csv( "~/Google Drive/Volumes/Research Project/Identities to Traits/Study 2/Cleaning/Output/id2traitShort.csv", header = T)
indDiff2 <- read.csv( "~/Google Drive/Volumes/Research Project/Identities to Traits/Study 2/Cleaning/Output/indDiff.csv", header = T)
idSim2 <- read.csv( "~/Google Drive/Volumes/Research Project/Identities to Traits/Study 2/Cleaning/Output/identitySimDf.csv", header = T)
#idToSim2 <- read.csv("~/Google Drive/Volumes/Research Project/Identities to Traits/Study 2/Cleaning/Output/simDf.csv", header = T)
# subset data for traits to only appear once per subject

traitsPerS1 <- fullLong1 %>% distinct(subID, Idx, .keep_all = TRUE)
traitsPerS2 <- fullLong2 %>% distinct(subID, Idx, .keep_all = TRUE)

# subset data for only connected traits to appear per subject

connectDf1 <- fullLong1 %>% filter(connect==1)
connectDf2 <- fullLong2 %>% filter(connect==1)

# convert to factors

fullLong1$connect <- as.factor(fullLong1$connect)
levels(fullLong1$connect) <- list(No  = "0", Yes = "1")

fullLong2$connect <- as.factor(fullLong2$connect)
levels(fullLong2$connect) <- list(No  = "0", Yes = "1")

# pos neg asymmetry

idShort1$pndiff <- idShort1$pI2Tdeg - idShort1$nI2Tdeg
idShort2$pndiff <- idShort2$pI2Tdeg - idShort2$nI2Tdeg

Identity Typicality

Traits that are nominated as typical of some identity are evaluated more self-descriptively

Study 1

summary(connect1)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(selfResp) ~ connect + (connect | subID) + (1 | traits)
   Data: fullLong1

REML criterion at convergence: 1399579

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-4.2589 -0.6865 -0.0179  0.6681  3.7713 

Random effects:
 Groups   Name        Variance Std.Dev. Corr 
 traits   (Intercept) 0.30258  0.5501        
 subID    (Intercept) 0.04080  0.2020        
          connectYes  0.05295  0.2301   -0.27
 Residual             0.64345  0.8022        
Number of obs: 582288, groups:  traits, 296; subID, 246

Fixed effects:
             Estimate Std. Error        df t value Pr(>|t|)    
(Intercept)  -0.01507    0.03449 386.04867  -0.437    0.662    
connectYes    0.18996    0.01553 249.72123  12.229   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
           (Intr)
connectYes -0.098

Study 2

connect2 <- lmer(scale(selfResp) ~ connect + subTend + traitTend + ( connect | subID ) + ( 1 | traits), data=fullLong2)
summary(connect2)
connect2.plot <- ggpredict(connect2, c("connect")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Identity-Typicality") + ylab("Self-Evaluation")
connect2.plot

Combined

plotCommAxes(connect1.plot, connect2.plot, "Connect", "Self-Evaluation")

Effect of Identity Typicality Depends on Identity Importance

Study 1

Identity importance defined by strength of identification. This is not significant for identity-to-identity centrality.

connect.streng1 <- lmer(scale(selfResp) ~ connect * scale(streng) + subTend + traitTend + ( connect + scale(streng) | subID ) + ( 1 | traits), data=fullLong1)
boundary (singular) fit: see help('isSingular')
connect.streng1 <- lmer(scale(selfResp) ~ connect * scale(streng) + subTend + traitTend + ( connect + scale(streng) | subID ) + ( 1 | traits), data=fullLong1)
boundary (singular) fit: see help('isSingular')
summary(connect.streng1)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(selfResp) ~ connect * scale(streng) + subTend + traitTend +  
    (connect + scale(streng) | subID) + (1 | traits)
   Data: fullLong1

REML criterion at convergence: 1399343

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-4.2607 -0.6863 -0.0183  0.6682  3.7875 

Random effects:
 Groups   Name          Variance  Std.Dev. Corr       
 traits   (Intercept)   1.418e-01 0.376537            
 subID    (Intercept)   4.060e-02 0.201488            
          connectYes    5.149e-02 0.226906 -0.26      
          scale(streng) 1.636e-05 0.004045  0.17 -1.00
 Residual               6.434e-01 0.802125            
Number of obs: 582288, groups:  traits, 296; subID, 246

Fixed effects:
                           Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)              -6.642e-01  4.490e-02  4.046e+02 -14.791  < 2e-16 ***
connectYes                1.860e-01  1.535e-02  2.508e+02  12.118  < 2e-16 ***
scale(streng)            -4.933e-03  1.294e-03  2.830e+03  -3.813  0.00014 ***
subTend                   6.169e-04  5.132e-04  2.430e+02   1.202  0.23051    
traitTend                 9.622e-01  5.261e-02  2.944e+02  18.289  < 2e-16 ***
connectYes:scale(streng)  2.741e-02  4.858e-03  2.184e+05   5.641 1.69e-08 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) cnnctY scl(s) subTnd trtTnd
connectYes  -0.065                            
scal(strng)  0.008 -0.187                     
subTend     -0.291 -0.015  0.006              
traitTend   -0.772 -0.003  0.001  0.000       
cnnctYs:s()  0.001 -0.039 -0.240 -0.004  0.000
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')

Study 2

connect.streng2 <- lmer(scale(selfResp) ~ connect * scale(streng) + ( connect + scale(streng) | subID ) + ( 1 | traits), data=fullLong2)
summary(connect.streng2)
connect.streng2.plot <- ggpredict(connect.streng2, c("streng","connect")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Strength of Identification") + ylab("Self-Evaluation")
connect.streng2.plot

Combined

plotCommAxes(connect.streng1.plot, connect.streng2.plot, "Strength of Identification", "Self-Evaluation")

Effect of Identity Typicality Depends on Size

Size Difference

connect.size2 <- lmer(scale(selfResp) ~ connect * scale(sizeD) + ( connect + scale(sizeD) | subID ) + ( 1 | traits), data=fullLong2)
summary(connect.size2)
connect.size2.plot <- ggpredict(connect.size2, c("sizeD","connect")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Size Differences") + ylab("Self-Evaluation")
connect.size2.plot

Identity Connections

Traits that are nominated as typical of more identities are evaluated more self-descriptively

Study 1

moconn1 <- lmer(scale(selfResp) ~ scale(IdIn) + ( scale(IdIn) | subID ) + ( 1 | traits), data=traitsPerS1)
summary(moconn1)
moconn1.plot <- ggpredict(moconn1, c("IdIn")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Identity-Associations") + ylab("Self-Evaluation")
moconn1.plot

Study 2

moconn2 <- lmer(scale(selfResp) ~ scale(IdIn) + ( scale(IdIn) | subID ) + ( 1 | traits), data=traitsPerS1)
summary(moconn2)
moconn2.plot <- ggpredict(moconn2, c("IdIn")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Identity-Associations") + ylab("Self-Evaluation")
moconn2.plot

Combined

plotCommAxes(moconn1.plot, moconn2.plot, "Identity-Typicality", "Self-Evaluation")

Identity Overlap

Study 1

sm1<-lmer(scale(selfResp) ~ scale(T.Sim) * scale(streng) + scale(subTend) + scale(traitTend)   + ( scale(order) | subID ) + ( 1 | traits), data=orderDf1, control=lmerControl(optimizer="bobyqa",
                                 optCtrl=list(maxfun=2e5)))
summary(sm1)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(selfResp) ~ scale(T.Sim) * scale(streng) + scale(subTend) +  
    scale(traitTend) + (scale(order) | subID) + (1 | traits)
   Data: orderDf1
Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2e+05))

REML criterion at convergence: 1354405

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-4.3908 -0.6758 -0.0174  0.6682  4.1252 

Random effects:
 Groups   Name         Variance Std.Dev. Corr
 traits   (Intercept)  0.13281  0.3644       
 subID    (Intercept)  0.04039  0.2010       
          scale(order) 0.01523  0.1234   0.21
 Residual              0.63400  0.7962       
Number of obs: 566898, groups:  traits, 296; subID, 246

Fixed effects:
                             Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)                 9.581e-03  2.465e-02  4.662e+02   0.389 0.697640    
scale(T.Sim)                4.057e-02  1.561e-03  4.781e+05  25.995  < 2e-16 ***
scale(streng)              -4.605e-03  1.263e-03  5.598e+05  -3.647 0.000265 ***
scale(subTend)              4.451e-03  1.261e-02  2.448e+02   0.353 0.724392    
scale(traitTend)            3.865e-01  2.121e-02  2.942e+02  18.219  < 2e-16 ***
scale(T.Sim):scale(streng)  2.308e-02  1.264e-03  5.631e+05  18.256  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) sc(T.S) scl(s) scl(sT) scl(tT)
scale(T.Sm) -0.009                               
scal(strng) -0.001 -0.075                        
scal(sbTnd)  0.000 -0.031   0.009                
scl(trtTnd)  0.000 -0.012   0.002  0.001         
scl(T.S):() -0.002 -0.126   0.093  0.006  -0.001 
sm1.plot <- ggpredict(sm1, c("T.Sim", "streng")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Overlap with Identity") + ylab("Self-Evaluation")
sm1.plot

Study 2

sm2<-lmer(scale(selfResp) ~ scale(T.Sim) * scale(streng) + scale(subTend) + scale(traitTend)  + ( scale(order) | subID ) + ( 1 | traits), data=orderDf2, control=lmerControl(optimizer="bobyqa",
                                 optCtrl=list(maxfun=2e5)))
summary(sm2)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(selfResp) ~ scale(T.Sim) * scale(streng) + scale(subTend) +  
    scale(traitTend) + (scale(order) | subID) + (1 | traits)
   Data: orderDf2
Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2e+05))

REML criterion at convergence: 1374296

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-4.1462 -0.6626 -0.0127  0.6630  4.2335 

Random effects:
 Groups   Name         Variance Std.Dev. Corr
 traits   (Intercept)  0.15046  0.3879       
 subID    (Intercept)  0.06164  0.2483       
          scale(order) 0.01028  0.1014   0.23
 Residual              0.63152  0.7947       
Number of obs: 576170, groups:  traits, 296; subID, 247

Fixed effects:
                             Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)                 1.821e-02  2.731e-02  5.000e+02   0.667  0.50534    
scale(T.Sim)                3.484e-02  1.521e-03  4.046e+05  22.903  < 2e-16 ***
scale(streng)              -3.705e-03  1.264e-03  5.723e+05  -2.930  0.00338 ** 
scale(subTend)              5.327e-03  1.541e-02  2.450e+02   0.346  0.72985    
scale(traitTend)            3.577e-01  2.257e-02  2.941e+02  15.847  < 2e-16 ***
scale(T.Sim):scale(streng)  1.042e-02  1.255e-03  5.642e+05   8.306  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) sc(T.S) scl(s) scl(sT) scl(tT)
scale(T.Sm) -0.014                               
scal(strng) -0.001 -0.048                        
scal(sbTnd)  0.000 -0.029   0.003                
scl(trtTnd)  0.000 -0.009   0.001  0.001         
scl(T.S):() -0.001 -0.096   0.066  0.003  -0.001 
sm2.plot <- ggpredict(sm2, c("T.Sim", "streng")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Overlap with Identity") + ylab("Self-Evaluation")
sm2.plot

Combined Plot

plotCommAxes(sm1.plot, sm2.plot, "Identity Overlap", "Self-Evaluation")

Identity Distance

Study 1

dm1<-lmer(scale(selfResp) ~ scale(order) * scale(streng) + scale(subTend) + scale(traitTend)  + ( scale(order) | subID ) + ( 1 | traits), data=orderDf1)
Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,  :
  Model failed to converge with max|grad| = 0.0027177 (tol = 0.002, component 1)
summary(dm1)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(selfResp) ~ scale(order) * scale(streng) + scale(subTend) +  
    scale(traitTend) + (scale(order) | subID) + (1 | traits)
   Data: orderDf1

REML criterion at convergence: 1355429

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-4.3472 -0.6756 -0.0174  0.6693  4.1093 

Random effects:
 Groups   Name         Variance Std.Dev. Corr
 traits   (Intercept)  0.13700  0.3701       
 subID    (Intercept)  0.03995  0.1999       
          scale(order) 0.01383  0.1176   0.18
 Residual              0.63517  0.7970       
Number of obs: 566898, groups:  traits, 296; subID, 246

Fixed effects:
                             Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)                -4.469e-03  2.503e-02  4.662e+02  -0.179  0.85838    
scale(order)               -6.429e-02  7.619e-03  2.474e+02  -8.438 2.73e-15 ***
scale(streng)              -4.114e-03  1.258e-03  5.599e+05  -3.272  0.00107 ** 
scale(subTend)              1.298e-02  1.260e-02  2.445e+02   1.031  0.30370    
scale(traitTend)            3.937e-01  2.154e-02  2.941e+02  18.276  < 2e-16 ***
scale(order):scale(streng) -9.300e-03  1.281e-03  4.868e+05  -7.261 3.85e-13 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) scl(r) scl(s) scl(sT) scl(tT)
scale(ordr)  0.091                              
scal(strng)  0.000  0.014                       
scal(sbTnd)  0.000  0.005  0.007                
scl(trtTnd)  0.000  0.002  0.001  0.000         
scl(rdr):()  0.002  0.005 -0.053  0.001   0.001 
optimizer (nloptwrap) convergence code: 0 (OK)
Model failed to converge with max|grad| = 0.0027177 (tol = 0.002, component 1)
dm1.plot <- ggpredict(dm1, c("order", "streng")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Distance from Identity") + ylab("Self-Evaluation")
dm1.plot

Study 2

dm2<-lmer(scale(selfResp) ~ scale(order) * scale(streng) + scale(subTend) + scale(traitTend)  + ( scale(order) | subID ) + ( 1 | traits), data=orderDf2)
Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,  :
  Model failed to converge with max|grad| = 0.0084477 (tol = 0.002, component 1)
summary(dm2)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(selfResp) ~ scale(order) * scale(streng) + scale(subTend) +  
    scale(traitTend) + (scale(order) | subID) + (1 | traits)
   Data: orderDf2

REML criterion at convergence: 1374845

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-4.0795 -0.6635 -0.0129  0.6634  4.2017 

Random effects:
 Groups   Name         Variance Std.Dev. Corr
 traits   (Intercept)  0.154257 0.39276      
 subID    (Intercept)  0.061307 0.24760      
          scale(order) 0.009124 0.09552  0.22
 Residual              0.632151 0.79508      
Number of obs: 576170, groups:  traits, 296; subID, 247

Fixed effects:
                             Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)                -2.815e-03  2.776e-02  5.023e+02  -0.101 0.919263    
scale(order)               -5.488e-02  6.222e-03  2.491e+02  -8.820  < 2e-16 ***
scale(streng)              -2.985e-03  1.263e-03  5.720e+05  -2.364 0.018082 *  
scale(subTend)              1.459e-02  1.543e-02  2.445e+02   0.945 0.345468    
scale(traitTend)            3.623e-01  2.285e-02  2.940e+02  15.852  < 2e-16 ***
scale(order):scale(streng) -4.490e-03  1.218e-03  4.239e+05  -3.685 0.000229 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) scl(r) scl(s) scl(sT) scl(tT)
scale(ordr)  0.121                              
scal(strng)  0.000  0.008                       
scal(sbTnd)  0.000  0.005  0.001                
scl(trtTnd)  0.000  0.002  0.000  0.000         
scl(rdr):()  0.000  0.001 -0.051  0.002   0.001 
optimizer (nloptwrap) convergence code: 0 (OK)
Model failed to converge with max|grad| = 0.0084477 (tol = 0.002, component 1)
dm2.plot <- ggpredict(dm2, c("order", "streng")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Distance from Identity") + ylab("Self-Evaluation")
dm2.plot

Combined Plot

plotCommAxes(dm1.plot, dm2.plot, "Identity Distance", "Self-Evaluation")

Identity Similarity and Distance Joint Influence on Self-Evaluations (Principal Component)

Study 1

pca1<-lmer(scale(selfResp) ~ scale(PCAdist) * scale(streng) + scale(subTend) + scale(traitTend) + ( scale(PCAdist) | subID ) + ( 1 | traits), data=orderDf1)
summary(pca1)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(selfResp) ~ scale(PCAdist) * scale(streng) + scale(subTend) +  
    scale(traitTend) + (scale(PCAdist) | subID) + (1 | traits)
   Data: orderDf1

REML criterion at convergence: 1348143

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-4.6482 -0.6714 -0.0188  0.6621  4.2617 

Random effects:
 Groups   Name           Variance Std.Dev. Corr 
 traits   (Intercept)    0.13137  0.3625        
 subID    (Intercept)    0.04217  0.2053        
          scale(PCAdist) 0.02131  0.1460   -0.24
 Residual                0.62694  0.7918        
Number of obs: 566898, groups:  traits, 296; subID, 246

Fixed effects:
                               Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)                  -6.219e-03  2.483e-02  4.778e+02  -0.250    0.802    
scale(PCAdist)                7.260e-02  9.418e-03  2.480e+02   7.708 3.08e-13 ***
scale(streng)                -6.255e-03  1.259e-03  5.596e+05  -4.969 6.74e-07 ***
scale(subTend)                6.149e-03  1.277e-02  2.444e+02   0.482    0.631    
scale(traitTend)              3.839e-01  2.110e-02  2.942e+02  18.197  < 2e-16 ***
scale(PCAdist):scale(streng)  9.040e-03  1.314e-03  5.294e+05   6.878 6.06e-12 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) sc(PCA) scl(s) scl(sT) scl(tT)
scl(PCAdst) -0.126                               
scal(strng)  0.000 -0.016                        
scal(sbTnd)  0.000 -0.007   0.008                
scl(trtTnd)  0.000 -0.003   0.002  0.001         
scl(PCA):() -0.003 -0.003   0.103 -0.001   0.000 
pca1.plot <- ggpredict(pca1, c("PCAdist", "streng")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Composite Identity-Overlap") + ylab("Self-Evaluation")
pca1.plot

Study 2

Combined Plot

Identity Centrality Predicts Strength of Identification

Study 1

I2I1.streng<-lmer(scale(streng) ~ scale(I2Ideg) + ( scale(I2Ideg) | subID ) + ( 1 | id), data=idShort1)
summary(I2I1.streng)
I2I1.streng.plot <- ggpredict(I2I1.streng, c("I2Ideg")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Identity-to-Identity Centrality") + ylab("Self-Evaluation")
I2I1.streng.plot

Study 2

I2I2.streng<-lmer(scale(streng) ~ scale(I2Ideg) + ( scale(I2Ideg) | subID ) + ( 1 | id), data=idShort2)
summary(I2I2.streng)
I2I2.streng.plot <- ggpredict(I2I2.streng, c("I2Ideg")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Identity-to-Identity Centrality") + ylab("Self-Evaluation")
I2I2.streng.plot

Combined Plot

plotCommAxes(I2I1.streng.plot, I2I2.streng.plot, "Identity-to-Identity Centrality", "Strength of Identification")

Strength of Identification Predicts More Shared Traits Between Identities

Study 1

TSharedStreng1 <- lmer( scale(traitCommNod) ~ scale(streng) + ( scale(streng)  | subID) + ( 1 | id), data=idShort1)
summary(TSharedStreng1)
TSharedStreng1.plot <- ggpredict(TSharedStreng1, c("streng")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Strength of Identification") + ylab("Shared Traits")

Study 2

TSharedStreng2 <- lmer( scale(traitCommNod) ~ scale(streng) + ( scale(streng)  | subID) + ( 1 | id), data=idShort2)
summary(TSharedStreng2)
TSharedStreng2.plot <- ggpredict(TSharedStreng2, c("streng")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Strength of Identification") + ylab("Shared Traits")

Combined Plot

ggpubr::ggarrange(TSharedStreng1.plot, TSharedStreng2.plot)
plotCommAxes(TSharedStreng1.plot, TSharedStreng2.plot, "Strength of Identification", "Proportion of Traits in Common")

Does positvity of identity predict asymmetries in valenced content?

Study 1

asym.pos1 <-lmer(pos ~ pndiff + ( pndiff | subID) + (1 | id), data=idShort1)
summary(asym.pos1)

Study 2

asym.pos2 <-lmer(pos ~ pndiff + ( pndiff | subID) + (1 | id), data=idShort2)
summary(asym.pos2)

More trait overlap predicts stronger group identification

Study 1

tcomm.streng1 <-lmer(scale(streng) ~ scale(traitCommNod) + ( scale(traitCommNod) | subID) + (1 | id), data=idShort1)
summary(tcomm.streng1)

Study 2

tcomm.streng2 <-lmer(scale(streng) ~ scale(traitCommNod) + ( scale(traitCommNod) | subID) + (1 | id), data=idShort2)
summary(tcomm.streng2)

More identity overlap predicts stronger group identification

Study 1

icomm.streng1 <-lmer(scale(streng) ~ scale(idCommNod) + ( scale(idCommNod) | subID) + (1 | id), data=idShort1)
summary(icomm.streng1)

Study 2

icomm.streng2 <-lmer(scale(streng) ~ scale(idCommNod) + ( scale(idCommNod) | subID) + (1 | id), data=idShort2)
summary(icomm.streng2)

Individual Differences Predict Similarity in Identity Judgments

m <-lmer(scale(posDist) ~ scale(SE) + ( scale(SE) | subID), data=idSim1)
summary(m)

m <-lmer(scale(strengDist) ~ scale(NFC) + ( scale(NFC) | subID), data=idSim1)
summary(m)

m <-lmer(scale(posDist) ~ scale(DS) + ( 1 | subID), data=idSim1)
summary(m)

m <-lmer(scale(posDist) ~ scale(SCC) + ( 1 | subID), data=idSim1)
summary(m)

m <-lmer(scale(posDist) ~ scale(MemSE) + ( 1 | subID), data=idSim1)
summary(m)

m <-lmer(scale(posDist) ~ scale(PrivCSE) + ( 1 | subID), data=idSim1)
summary(m)

m <-lmer(scale(posDist) ~ scale(PubCSE) + ( 1 | subID), data=idSim1)
summary(m)

m <-lmer(scale(posDist) ~ scale(MemSE) + ( 1 | subID), data=idSim1)
summary(m)

Individual Differences Predict Pairwise Overlap in Traits in Common

Study 1

Positive

m <-lmer(scale(idtpSim) ~ scale(SCC) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim1)
summary(m)

m <-lmer(scale(idtpSim) ~ scale(Ind) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim1)
summary(m)

m <-lmer(scale(idtpSim) ~ scale(Inter) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim1)
summary(m)

m <-lmer(scale(idtpSim) ~ scale(SWLS) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim1)
summary(m)

m <-lmer(scale(idtpSim) ~ scale(IdImp) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim1)
summary(m)

m <-lmer(scale(idtpSim) ~ scale(phi) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim1)
summary(m)

m <-lmer(scale(idtpSim) ~ scale(overlap_norm) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim1)
summary(m)

m <-lmer(scale(idtpSim) ~ scale(H_index) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim1)
summary(m)

m <-lmer(scale(idtpSim) ~ scale(SE) + scale(idtnSim) + ( scale(idtnSim)| subID), data=idSim1)
summary(m)

m <-lmer(scale(idtpSim) ~ scale(NFC) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim1)
summary(m)

m <-lmer(scale(idtpSim) ~ scale(DS) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim1)
summary(m)

m <-lmer(scale(idtpSim) ~ scale(SCC) + scale(idtnSim) + ( scale(idtnSim)| subID), data=idSim1)
summary(m)

m <-lmer(scale(idtpSim) ~ scale(MemSE) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim1)
summary(m)

m <-lmer(scale(idtpSim) ~ scale(PrivCSE) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim1)
summary(m)

m <-lmer(scale(idtpSim) ~ scale(PubCSE) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim1)
summary(m)

m <-lmer(scale(idtpSim) ~ scale(MemSE) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim1)
summary(m)

Negative

m <-lmer(scale(idtnSim) ~ scale(SCC) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim1)
summary(m)

m <-lmer(scale(idtnSim) ~ scale(Ind) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim1)
summary(m)

m <-lmer(scale(idtnSim) ~ scale(Inter) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim1)
summary(m)

m <-lmer(scale(idtnSim) ~ scale(SWLS) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim1)
summary(m)

m <-lmer(scale(idtnSim) ~ scale(IdImp) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim1)
summary(m)

m <-lmer(scale(idtnSim) ~ scale(phi) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim1)
summary(m)

m <-lmer(scale(idtnSim) ~ scale(overlap_norm) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim1)
summary(m)

m <-lmer(scale(idtnSim) ~ scale(H_index) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim1)
summary(m)

m <-lmer(scale(idtnSim) ~ scale(SE) + scale(idtpSim) + ( scale(idtpSim)| subID), data=idSim1)
summary(m)

m <-lmer(scale(idtnSim) ~ scale(NFC) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim1)
summary(m)

m <-lmer(scale(idtnSim) ~ scale(DS) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim1)
summary(m)

m <-lmer(scale(idtnSim) ~ scale(SCC) + scale(idtpSim) + ( scale(idtpSim)| subID), data=idSim1)
summary(m)

m <-lmer(scale(idtnSim) ~ scale(MemSE) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim1)
summary(m)

m <-lmer(scale(idtnSim) ~ scale(PrivCSE) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim1)
summary(m)

m <-lmer(scale(idtnSim) ~ scale(PubCSE) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim1)
summary(m)

m <-lmer(scale(idtnSim) ~ scale(MemSE) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim1)
summary(m)

Both

m <-lmer(scale(idtSim) ~ scale(SCC) + ( 1 | subID), data=idSim1)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtSim) ~ scale(SCC) + (1 | subID)
   Data: idSim1

REML criterion at convergence: 16558.8

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.2646 -0.6147 -0.1921  0.5184  7.7602 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.4219   0.6495  
 Residual             0.5803   0.7618  
Number of obs: 6888, groups:  subID, 246

Fixed effects:
              Estimate Std. Error         df t value Pr(>|t|)
(Intercept) -7.412e-15  4.242e-02  2.440e+02   0.000    1.000
scale(SCC)   3.530e-02  4.242e-02  2.440e+02   0.832    0.406

Correlation of Fixed Effects:
           (Intr)
scale(SCC) 0.000 
m <-lmer(scale(idtSim) ~ scale(Ind) + ( 1 | subID), data=idSim1)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtSim) ~ scale(Ind) + (1 | subID)
   Data: idSim1

REML criterion at convergence: 16545.1

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.2779 -0.6182 -0.1888  0.5172  7.7610 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.3977   0.6307  
 Residual             0.5803   0.7618  
Number of obs: 6888, groups:  subID, 246

Fixed effects:
              Estimate Std. Error         df t value Pr(>|t|)    
(Intercept) -5.741e-15  4.124e-02  2.440e+02   0.000  1.00000    
scale(Ind)   1.588e-01  4.125e-02  2.440e+02   3.851  0.00015 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
           (Intr)
scale(Ind) 0.000 
m <-lmer(scale(idtSim) ~ scale(Inter) + ( 1 | subID), data=idSim1)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtSim) ~ scale(Inter) + (1 | subID)
   Data: idSim1

REML criterion at convergence: 16554.3

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.2693 -0.6133 -0.1927  0.5197  7.7622 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.4137   0.6432  
 Residual             0.5803   0.7618  
Number of obs: 6888, groups:  subID, 246

Fixed effects:
               Estimate Std. Error         df t value Pr(>|t|)  
(Intercept)  -5.445e-15  4.202e-02  2.440e+02   0.000   1.0000  
scale(Inter)  9.676e-02  4.203e-02  2.440e+02   2.302   0.0222 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr)
scale(Intr) 0.000 
m <-lmer(scale(idtSim) ~ scale(SWLS) + ( 1 | subID), data=idSim1)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtSim) ~ scale(SWLS) + (1 | subID)
   Data: idSim1

REML criterion at convergence: 16555.3

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.2661 -0.6148 -0.1935  0.5173  7.7605 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.4156   0.6447  
 Residual             0.5803   0.7618  
Number of obs: 6888, groups:  subID, 246

Fixed effects:
              Estimate Std. Error         df t value Pr(>|t|)  
(Intercept) -5.251e-15  4.212e-02  2.440e+02   0.000   1.0000  
scale(SWLS)  8.645e-02  4.212e-02  2.440e+02   2.053   0.0412 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr)
scale(SWLS) 0.000 
m <-lmer(scale(idtSim) ~ scale(IdImp) + ( 1 | subID), data=idSim1)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtSim) ~ scale(IdImp) + (1 | subID)
   Data: idSim1

REML criterion at convergence: 16559.3

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.2607 -0.6126 -0.1920  0.5190  7.7619 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.4228   0.6502  
 Residual             0.5803   0.7618  
Number of obs: 6888, groups:  subID, 246

Fixed effects:
               Estimate Std. Error         df t value Pr(>|t|)
(Intercept)  -8.017e-15  4.246e-02  2.440e+02   0.000    1.000
scale(IdImp)  1.843e-02  4.247e-02  2.440e+02   0.434    0.665

Correlation of Fixed Effects:
            (Intr)
scal(IdImp) 0.000 
m <-lmer(scale(idtSim) ~ scale(phi) + ( 1 | subID), data=idSim1)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtSim) ~ scale(phi) + (1 | subID)
   Data: idSim1

REML criterion at convergence: 16555

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.2605 -0.6137 -0.1879  0.5196  7.7573 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.4150   0.6442  
 Residual             0.5803   0.7618  
Number of obs: 6888, groups:  subID, 246

Fixed effects:
              Estimate Std. Error         df t value Pr(>|t|)  
(Intercept) -7.702e-15  4.209e-02  2.440e+02   0.000   1.0000  
scale(phi)  -8.981e-02  4.209e-02  2.440e+02  -2.134   0.0339 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
           (Intr)
scale(phi) 0.000 
m <-lmer(scale(idtSim) ~ scale(overlap_norm) + ( 1 | subID), data=idSim1)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtSim) ~ scale(overlap_norm) + (1 | subID)
   Data: idSim1

REML criterion at convergence: 16011.9

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.6606 -0.6123 -0.1771  0.5399  7.8079 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.02633  0.1623  
 Residual             0.58030  0.7618  
Number of obs: 6888, groups:  subID, 246

Fixed effects:
                      Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)         -9.045e-16  1.383e-02  2.440e+02    0.00        1    
scale(overlap_norm)  6.274e-01  1.383e-02  2.440e+02   45.36   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr)
scl(vrlp_n) 0.000 
m <-lmer(scale(idtSim) ~ scale(H_index) + ( 1 | subID), data=idSim1)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtSim) ~ scale(H_index) + (1 | subID)
   Data: idSim1

REML criterion at convergence: 16340.3

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.0934 -0.5980 -0.1875  0.5102  7.8369 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.1600   0.4000  
 Residual             0.5803   0.7618  
Number of obs: 6888, groups:  subID, 246

Fixed effects:
                 Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)    -4.486e-15  2.711e-02  2.440e+02    0.00        1    
scale(H_index)  5.109e-01  2.711e-02  2.440e+02   18.85   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr)
scal(H_ndx) 0.000 
m <-lmer(scale(idtSim) ~ scale(SE) + ( 1| subID), data=idSim1)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtSim) ~ scale(SE) + (1 | subID)
   Data: idSim1

REML criterion at convergence: 16496.1

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.2653 -0.6157 -0.1894  0.5188  7.7542 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.4167   0.6455  
 Residual             0.5809   0.7622  
Number of obs: 6860, groups:  subID, 245

Fixed effects:
             Estimate Std. Error        df t value Pr(>|t|)  
(Intercept) 2.734e-03  4.226e-02 2.430e+02   0.065   0.9485  
scale(SE)   7.968e-02  4.226e-02 2.430e+02   1.885   0.0606 .
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
          (Intr)
scale(SE) 0.000 
m <-lmer(scale(idtSim) ~ scale(NFC) + ( 1 | subID), data=idSim1)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtSim) ~ scale(NFC) + (1 | subID)
   Data: idSim1

REML criterion at convergence: 16551.1

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.2728 -0.6170 -0.1912  0.5182  7.7561 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.4081   0.6388  
 Residual             0.5803   0.7618  
Number of obs: 6888, groups:  subID, 246

Fixed effects:
              Estimate Std. Error         df t value Pr(>|t|)   
(Intercept) -4.217e-15  4.175e-02  2.440e+02   0.000  1.00000   
scale(NFC)   1.223e-01  4.175e-02  2.440e+02   2.929  0.00372 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
           (Intr)
scale(NFC) 0.000 
m <-lmer(scale(idtSim) ~ scale(DS) + ( 1 | subID), data=idSim1)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtSim) ~ scale(DS) + (1 | subID)
   Data: idSim1

REML criterion at convergence: 16557.3

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.2683 -0.6150 -0.1919  0.5192  7.7594 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.4192   0.6475  
 Residual             0.5803   0.7618  
Number of obs: 6888, groups:  subID, 246

Fixed effects:
              Estimate Std. Error         df t value Pr(>|t|)
(Intercept) -5.432e-15  4.229e-02  2.440e+02    0.00     1.00
scale(DS)   -6.260e-02  4.229e-02  2.440e+02   -1.48     0.14

Correlation of Fixed Effects:
          (Intr)
scale(DS) 0.000 
m <-lmer(scale(idtSim) ~ scale(SCC) + ( 1| subID), data=idSim1)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtSim) ~ scale(SCC) + (1 | subID)
   Data: idSim1

REML criterion at convergence: 16558.8

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.2646 -0.6147 -0.1921  0.5184  7.7602 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.4219   0.6495  
 Residual             0.5803   0.7618  
Number of obs: 6888, groups:  subID, 246

Fixed effects:
              Estimate Std. Error         df t value Pr(>|t|)
(Intercept) -7.412e-15  4.242e-02  2.440e+02   0.000    1.000
scale(SCC)   3.530e-02  4.242e-02  2.440e+02   0.832    0.406

Correlation of Fixed Effects:
           (Intr)
scale(SCC) 0.000 
m <-lmer(scale(idtSim) ~ scale(MemSE) + ( 1 | subID), data=idSim1)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtSim) ~ scale(MemSE) + (1 | subID)
   Data: idSim1

REML criterion at convergence: 16558.1

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.2624 -0.6133 -0.1924  0.5193  7.7627 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.4205   0.6485  
 Residual             0.5803   0.7618  
Number of obs: 6888, groups:  subID, 246

Fixed effects:
               Estimate Std. Error         df t value Pr(>|t|)
(Intercept)  -6.489e-15  4.235e-02  2.440e+02   0.000     1.00
scale(MemSE)  5.099e-02  4.236e-02  2.440e+02   1.204     0.23

Correlation of Fixed Effects:
            (Intr)
scale(MmSE) 0.000 
m <-lmer(scale(idtSim) ~ scale(PrivCSE) + ( 1 | subID), data=idSim1)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtSim) ~ scale(PrivCSE) + (1 | subID)
   Data: idSim1

REML criterion at convergence: 16555.6

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.2557 -0.6146 -0.1910  0.5213  7.7609 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.4161   0.6450  
 Residual             0.5803   0.7618  
Number of obs: 6888, groups:  subID, 246

Fixed effects:
                 Estimate Std. Error         df t value Pr(>|t|)  
(Intercept)    -8.270e-15  4.214e-02  2.440e+02    0.00   1.0000  
scale(PrivCSE)  8.384e-02  4.214e-02  2.440e+02    1.99   0.0478 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr)
scl(PrvCSE) 0.000 
m <-lmer(scale(idtSim) ~ scale(PubCSE) + ( 1 | subID), data=idSim1)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtSim) ~ scale(PubCSE) + (1 | subID)
   Data: idSim1

REML criterion at convergence: 16554.1

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.2656 -0.6131 -0.1904  0.5211  7.7600 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.4135   0.6431  
 Residual             0.5803   0.7618  
Number of obs: 6888, groups:  subID, 246

Fixed effects:
                Estimate Std. Error         df t value Pr(>|t|)  
(Intercept)   -7.784e-15  4.201e-02  2.440e+02   0.000   1.0000  
scale(PubCSE)  9.779e-02  4.202e-02  2.440e+02   2.327   0.0208 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr)
scal(PbCSE) 0.000 
m <-lmer(scale(idtSim) ~ scale(MemSE) + ( 1 | subID), data=idSim1)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtSim) ~ scale(MemSE) + (1 | subID)
   Data: idSim1

REML criterion at convergence: 16558.1

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.2624 -0.6133 -0.1924  0.5193  7.7627 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.4205   0.6485  
 Residual             0.5803   0.7618  
Number of obs: 6888, groups:  subID, 246

Fixed effects:
               Estimate Std. Error         df t value Pr(>|t|)
(Intercept)  -6.489e-15  4.235e-02  2.440e+02   0.000     1.00
scale(MemSE)  5.099e-02  4.236e-02  2.440e+02   1.204     0.23

Correlation of Fixed Effects:
            (Intr)
scale(MmSE) 0.000 

Individual Differences Predict Pairwise Overlap in Identities in Common

m <-lmer(scale(idSim) ~ scale(SCC) + ( 1 | subID), data=idSim1)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idSim) ~ scale(SCC) + (1 | subID)
   Data: idSim1

REML criterion at convergence: 15808.4

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.2437 -0.6057 -0.1938  0.3497  4.2370 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.4882   0.6987  
 Residual             0.5157   0.7181  
Number of obs: 6888, groups:  subID, 246

Fixed effects:
              Estimate Std. Error         df t value Pr(>|t|)
(Intercept)  4.883e-15  4.538e-02  2.440e+02   0.000    1.000
scale(SCC)  -1.252e-03  4.539e-02  2.440e+02  -0.028    0.978

Correlation of Fixed Effects:
           (Intr)
scale(SCC) 0.000 
m <-lmer(scale(idSim) ~ scale(Ind) + ( 1 | subID), data=idSim1)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idSim) ~ scale(Ind) + (1 | subID)
   Data: idSim1

REML criterion at convergence: 15808.4

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.2427 -0.6055 -0.1928  0.3496  4.2380 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.4881   0.6987  
 Residual             0.5157   0.7181  
Number of obs: 6888, groups:  subID, 246

Fixed effects:
              Estimate Std. Error         df t value Pr(>|t|)
(Intercept)  9.267e-15  4.538e-02  2.440e+02   0.000    1.000
scale(Ind)  -1.030e-02  4.538e-02  2.440e+02  -0.227    0.821

Correlation of Fixed Effects:
           (Intr)
scale(Ind) 0.000 
m <-lmer(scale(idSim) ~ scale(Inter) + ( 1 | subID), data=idSim1)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idSim) ~ scale(Inter) + (1 | subID)
   Data: idSim1

REML criterion at convergence: 15807.8

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.2452 -0.6055 -0.1906  0.3462  4.2403 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.4870   0.6978  
 Residual             0.5157   0.7181  
Number of obs: 6888, groups:  subID, 246

Fixed effects:
              Estimate Std. Error        df t value Pr(>|t|)
(Intercept)  7.909e-15  4.533e-02 2.440e+02   0.000    1.000
scale(Inter) 3.543e-02  4.533e-02 2.440e+02   0.782    0.435

Correlation of Fixed Effects:
            (Intr)
scale(Intr) 0.000 
m <-lmer(scale(idSim) ~ scale(SWLS) + ( 1 | subID), data=idSim1)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idSim) ~ scale(SWLS) + (1 | subID)
   Data: idSim1

REML criterion at convergence: 15808.3

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.2447 -0.6058 -0.1927  0.3491  4.2381 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.4881   0.6986  
 Residual             0.5157   0.7181  
Number of obs: 6888, groups:  subID, 246

Fixed effects:
             Estimate Std. Error        df t value Pr(>|t|)
(Intercept) 8.683e-15  4.537e-02 2.440e+02   0.000    1.000
scale(SWLS) 1.320e-02  4.538e-02 2.440e+02   0.291    0.771

Correlation of Fixed Effects:
            (Intr)
scale(SWLS) 0.000 
m <-lmer(scale(idSim) ~ scale(IdImp) + ( 1 | subID), data=idSim1)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idSim) ~ scale(IdImp) + (1 | subID)
   Data: idSim1

REML criterion at convergence: 15806.5

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.2438 -0.6058 -0.1869  0.3510  4.2439 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.4842   0.6959  
 Residual             0.5157   0.7181  
Number of obs: 6888, groups:  subID, 246

Fixed effects:
              Estimate Std. Error        df t value Pr(>|t|)
(Intercept)  4.354e-15  4.520e-02 2.440e+02   0.000    1.000
scale(IdImp) 6.303e-02  4.521e-02 2.440e+02   1.394    0.165

Correlation of Fixed Effects:
            (Intr)
scal(IdImp) 0.000 
m <-lmer(scale(idSim) ~ scale(phi) + ( 1 | subID), data=idSim1)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idSim) ~ scale(phi) + (1 | subID)
   Data: idSim1

REML criterion at convergence: 15806.7

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.2458 -0.6070 -0.1889  0.3487  4.2419 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.4846   0.6961  
 Residual             0.5157   0.7181  
Number of obs: 6888, groups:  subID, 246

Fixed effects:
              Estimate Std. Error         df t value Pr(>|t|)
(Intercept)  7.393e-15  4.522e-02  2.440e+02   0.000    1.000
scale(phi)  -6.035e-02  4.522e-02  2.440e+02  -1.335    0.183

Correlation of Fixed Effects:
           (Intr)
scale(phi) 0.000 
m <-lmer(scale(idSim) ~ scale(overlap_norm) + ( 1 | subID), data=idSim1)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idSim) ~ scale(overlap_norm) + (1 | subID)
   Data: idSim1

REML criterion at convergence: 15791.3

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.2373 -0.6025 -0.1833  0.3453  4.2475 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.4540   0.6738  
 Residual             0.5157   0.7181  
Number of obs: 6888, groups:  subID, 246

Fixed effects:
                     Estimate Std. Error        df t value Pr(>|t|)    
(Intercept)         4.184e-15  4.382e-02 2.440e+02   0.000        1    
scale(overlap_norm) 1.843e-01  4.382e-02 2.440e+02   4.206 3.65e-05 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr)
scl(vrlp_n) 0.000 
m <-lmer(scale(idSim) ~ scale(H_index) + ( 1 | subID), data=idSim1)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idSim) ~ scale(H_index) + (1 | subID)
   Data: idSim1

REML criterion at convergence: 15781.4

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.2312 -0.6063 -0.1814  0.3389  4.2524 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.4352   0.6597  
 Residual             0.5157   0.7181  
Number of obs: 6888, groups:  subID, 246

Fixed effects:
                Estimate Std. Error        df t value Pr(>|t|)    
(Intercept)    5.340e-15  4.294e-02 2.440e+02   0.000        1    
scale(H_index) 2.294e-01  4.294e-02 2.440e+02   5.342 2.11e-07 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr)
scal(H_ndx) 0.000 
m <-lmer(scale(idSim) ~ scale(SE) + ( 1| subID), data=idSim1)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idSim) ~ scale(SE) + (1 | subID)
   Data: idSim1

REML criterion at convergence: 15736

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.2444 -0.6061 -0.1920  0.3399  4.2418 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.490    0.7000  
 Residual             0.515    0.7177  
Number of obs: 6860, groups:  subID, 245

Fixed effects:
              Estimate Std. Error         df t value Pr(>|t|)
(Intercept) -6.381e-04  4.555e-02  2.430e+02  -0.014    0.989
scale(SE)   -1.679e-02  4.555e-02  2.430e+02  -0.369    0.713

Correlation of Fixed Effects:
          (Intr)
scale(SE) 0.000 
m <-lmer(scale(idSim) ~ scale(NFC) + ( 1 | subID), data=idSim1)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idSim) ~ scale(NFC) + (1 | subID)
   Data: idSim1

REML criterion at convergence: 15807.8

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.2458 -0.6076 -0.1894  0.3492  4.2414 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.4870   0.6979  
 Residual             0.5157   0.7181  
Number of obs: 6888, groups:  subID, 246

Fixed effects:
             Estimate Std. Error        df t value Pr(>|t|)
(Intercept) 8.478e-15  4.533e-02 2.440e+02   0.000    1.000
scale(NFC)  3.482e-02  4.533e-02 2.440e+02   0.768    0.443

Correlation of Fixed Effects:
           (Intr)
scale(NFC) 0.000 
m <-lmer(scale(idSim) ~ scale(DS) + ( 1 | subID), data=idSim1)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idSim) ~ scale(DS) + (1 | subID)
   Data: idSim1

REML criterion at convergence: 15807.5

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.2496 -0.6042 -0.1896  0.3510  4.2412 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.4864   0.6974  
 Residual             0.5157   0.7181  
Number of obs: 6888, groups:  subID, 246

Fixed effects:
              Estimate Std. Error         df t value Pr(>|t|)
(Intercept)  1.022e-14  4.530e-02  2.440e+02   0.000    1.000
scale(DS)   -4.289e-02  4.530e-02  2.440e+02  -0.947    0.345

Correlation of Fixed Effects:
          (Intr)
scale(DS) 0.000 
m <-lmer(scale(idSim) ~ scale(SCC) + ( 1| subID), data=idSim1)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idSim) ~ scale(SCC) + (1 | subID)
   Data: idSim1

REML criterion at convergence: 15808.4

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.2437 -0.6057 -0.1938  0.3497  4.2370 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.4882   0.6987  
 Residual             0.5157   0.7181  
Number of obs: 6888, groups:  subID, 246

Fixed effects:
              Estimate Std. Error         df t value Pr(>|t|)
(Intercept)  4.883e-15  4.538e-02  2.440e+02   0.000    1.000
scale(SCC)  -1.252e-03  4.539e-02  2.440e+02  -0.028    0.978

Correlation of Fixed Effects:
           (Intr)
scale(SCC) 0.000 
m <-lmer(scale(idSim) ~ scale(MemSE) + ( 1 | subID), data=idSim1)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idSim) ~ scale(MemSE) + (1 | subID)
   Data: idSim1

REML criterion at convergence: 15808.4

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.2440 -0.6057 -0.1932  0.3499  4.2376 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.4882   0.6987  
 Residual             0.5157   0.7181  
Number of obs: 6888, groups:  subID, 246

Fixed effects:
              Estimate Std. Error        df t value Pr(>|t|)
(Intercept)  9.275e-15  4.538e-02 2.440e+02   0.000    1.000
scale(MemSE) 6.208e-03  4.538e-02 2.440e+02   0.137    0.891

Correlation of Fixed Effects:
            (Intr)
scale(MmSE) 0.000 
m <-lmer(scale(idSim) ~ scale(PrivCSE) + ( 1 | subID), data=idSim1)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idSim) ~ scale(PrivCSE) + (1 | subID)
   Data: idSim1

REML criterion at convergence: 15807.4

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.2471 -0.6060 -0.1898  0.3511  4.2410 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.4861   0.6972  
 Residual             0.5157   0.7181  
Number of obs: 6888, groups:  subID, 246

Fixed effects:
                Estimate Std. Error        df t value Pr(>|t|)
(Intercept)    1.105e-14  4.529e-02 2.440e+02   0.000    1.000
scale(PrivCSE) 4.581e-02  4.529e-02 2.440e+02   1.011    0.313

Correlation of Fixed Effects:
            (Intr)
scl(PrvCSE) 0.000 
m <-lmer(scale(idSim) ~ scale(PubCSE) + ( 1 | subID), data=idSim1)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idSim) ~ scale(PubCSE) + (1 | subID)
   Data: idSim1

REML criterion at convergence: 15808.4

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.2440 -0.6057 -0.1939  0.3497  4.2369 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.4882   0.6987  
 Residual             0.5157   0.7181  
Number of obs: 6888, groups:  subID, 246

Fixed effects:
               Estimate Std. Error        df t value Pr(>|t|)
(Intercept)   5.889e-15  4.538e-02 2.440e+02   0.000    1.000
scale(PubCSE) 2.111e-03  4.539e-02 2.440e+02   0.047    0.963

Correlation of Fixed Effects:
            (Intr)
scal(PbCSE) 0.000 
m <-lmer(scale(idSim) ~ scale(MemSE) + ( 1 | subID), data=idSim1)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idSim) ~ scale(MemSE) + (1 | subID)
   Data: idSim1

REML criterion at convergence: 15808.4

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.2440 -0.6057 -0.1932  0.3499  4.2376 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.4882   0.6987  
 Residual             0.5157   0.7181  
Number of obs: 6888, groups:  subID, 246

Fixed effects:
              Estimate Std. Error        df t value Pr(>|t|)
(Intercept)  9.275e-15  4.538e-02 2.440e+02   0.000    1.000
scale(MemSE) 6.208e-03  4.538e-02 2.440e+02   0.137    0.891

Correlation of Fixed Effects:
            (Intr)
scale(MmSE) 0.000 

Study 2

Positive

m <-lmer(scale(idtpSim) ~ scale(SCC) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim2)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtpSim) ~ scale(SCC) + scale(idtnSim) + (scale(idtnSim) |      subID)
   Data: idSim2

REML criterion at convergence: 12904

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.5877 -0.5984 -0.2681  0.5335  7.0228 

Random effects:
 Groups   Name           Variance Std.Dev. Corr
 subID    (Intercept)    0.3652   0.6043       
          scale(idtnSim) 0.0116   0.1077   0.40
 Residual                0.5902   0.7683       
Number of obs: 5320, groups:  subID, 190

Fixed effects:
                Estimate Std. Error        df t value Pr(>|t|)    
(Intercept)     -0.01131    0.04528 182.04489  -0.250 0.803065    
scale(SCC)       0.01090    0.04434 165.33351   0.246 0.806200    
scale(idtnSim)   0.06080    0.01590 212.20307   3.824 0.000173 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) s(SCC)
scale(SCC)   0.002       
scal(dtnSm)  0.195 -0.005
m <-lmer(scale(idtpSim) ~ scale(Ind) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim2)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtpSim) ~ scale(Ind) + scale(idtnSim) + (scale(idtnSim) |      subID)
   Data: idSim2

REML criterion at convergence: 12903.6

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.5883 -0.6000 -0.2694  0.5336  7.0241 

Random effects:
 Groups   Name           Variance Std.Dev. Corr
 subID    (Intercept)    0.36414  0.6034       
          scale(idtnSim) 0.01159  0.1077   0.40
 Residual                0.59026  0.7683       
Number of obs: 5320, groups:  subID, 190

Fixed effects:
                Estimate Std. Error        df t value Pr(>|t|)    
(Intercept)     -0.01144    0.04522 181.86420  -0.253 0.800564    
scale(Ind)       0.02942    0.04405 160.54157   0.668 0.505215    
scale(idtnSim)   0.06094    0.01590 212.20479   3.833 0.000167 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) scl(I)
scale(Ind)  -0.002       
scal(dtnSm)  0.196  0.006
m <-lmer(scale(idtpSim) ~ scale(Inter) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim2)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtpSim) ~ scale(Inter) + scale(idtnSim) + (scale(idtnSim) |      subID)
   Data: idSim2

REML criterion at convergence: 12903.7

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.5873 -0.5987 -0.2712  0.5337  7.0171 

Random effects:
 Groups   Name           Variance Std.Dev. Corr
 subID    (Intercept)    0.36502  0.6042       
          scale(idtnSim) 0.01155  0.1075   0.40
 Residual                0.59024  0.7683       
Number of obs: 5320, groups:  subID, 190

Fixed effects:
                Estimate Std. Error        df t value Pr(>|t|)    
(Intercept)     -0.01130    0.04527 182.18856  -0.250 0.803108    
scale(Inter)     0.02522    0.04446 168.46650   0.567 0.571338    
scale(idtnSim)   0.06079    0.01589 212.56900   3.825 0.000172 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) scl(I)
scale(Intr)  0.002       
scal(dtnSm)  0.195 -0.004
m <-lmer(scale(idtpSim) ~ scale(SWLS) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim2)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtpSim) ~ scale(SWLS) + scale(idtnSim) + (scale(idtnSim) |      subID)
   Data: idSim2

REML criterion at convergence: 12903.9

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.5866 -0.5976 -0.2692  0.5322  7.0200 

Random effects:
 Groups   Name           Variance Std.Dev. Corr
 subID    (Intercept)    0.3649   0.6041       
          scale(idtnSim) 0.0116   0.1077   0.39
 Residual                0.5902   0.7683       
Number of obs: 5320, groups:  subID, 190

Fixed effects:
                Estimate Std. Error        df t value Pr(>|t|)    
(Intercept)     -0.01128    0.04526 182.00258  -0.249  0.80342    
scale(SWLS)     -0.01474    0.04434 165.32795  -0.332  0.74000    
scale(idtnSim)   0.06087    0.01590 212.11200   3.828  0.00017 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) s(SWLS
scale(SWLS)  0.003       
scal(dtnSm)  0.192 -0.011
m <-lmer(scale(idtpSim) ~ scale(IdImp) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim2)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtpSim) ~ scale(IdImp) + scale(idtnSim) + (scale(idtnSim) |      subID)
   Data: idSim2

REML criterion at convergence: 12901.7

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.5870 -0.5975 -0.2690  0.5323  7.0234 

Random effects:
 Groups   Name           Variance Std.Dev. Corr
 subID    (Intercept)    0.36008  0.6001       
          scale(idtnSim) 0.01158  0.1076   0.39
 Residual                0.59022  0.7683       
Number of obs: 5320, groups:  subID, 190

Fixed effects:
                Estimate Std. Error        df t value Pr(>|t|)    
(Intercept)     -0.01113    0.04498 181.96470  -0.248 0.804771    
scale(IdImp)     0.06742    0.04396 163.08481   1.534 0.127088    
scale(idtnSim)   0.06081    0.01590 212.13012   3.826 0.000172 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) sc(II)
scal(IdImp)  0.001       
scal(dtnSm)  0.190 -0.007
m <-lmer(scale(idtpSim) ~ scale(phi) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim2)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtpSim) ~ scale(phi) + scale(idtnSim) + (scale(idtnSim) |      subID)
   Data: idSim2

REML criterion at convergence: 12898.7

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.5739 -0.6014 -0.2704  0.5311  7.0444 

Random effects:
 Groups   Name           Variance Std.Dev. Corr
 subID    (Intercept)    0.35334  0.5944       
          scale(idtnSim) 0.01149  0.1072   0.35
 Residual                0.59013  0.7682       
Number of obs: 5320, groups:  subID, 190

Fixed effects:
                 Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)     -0.009882   0.044591 182.205240  -0.222 0.824872    
scale(phi)      -0.103144   0.043795 165.969844  -2.355 0.019683 *  
scale(idtnSim)   0.059839   0.015895 211.196853   3.765 0.000216 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) scl(p)
scale(phi)  -0.006       
scal(dtnSm)  0.169  0.034
m <-lmer(scale(idtpSim) ~ scale(overlap_norm) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim2)
Model failed to converge with max|grad| = 0.00351062 (tol = 0.002, component 1)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtpSim) ~ scale(overlap_norm) + scale(idtnSim) + (scale(idtnSim) |      subID)
   Data: idSim2

REML criterion at convergence: 12545.4

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.5885 -0.6210 -0.2473  0.5394  6.9553 

Random effects:
 Groups   Name           Variance Std.Dev. Corr 
 subID    (Intercept)    0.034411 0.18550       
          scale(idtnSim) 0.008097 0.08998  -0.08
 Residual                0.590868 0.76868       
Number of obs: 5320, groups:  subID, 190

Fixed effects:
                     Estimate Std. Error        df t value Pr(>|t|)    
(Intercept)         5.021e-05  1.750e-02 1.850e+02   0.003 0.997715    
scale(overlap_norm) 5.902e-01  1.778e-02 1.805e+02  33.204  < 2e-16 ***
scale(idtnSim)      5.324e-02  1.416e-02 2.447e+02   3.758 0.000214 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) scl(_)
scl(vrlp_n)  0.011       
scal(dtnSm) -0.023 -0.153
optimizer (nloptwrap) convergence code: 0 (OK)
Model failed to converge with max|grad| = 0.00351062 (tol = 0.002, component 1)
m <-lmer(scale(idtpSim) ~ scale(H_index) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim2)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtpSim) ~ scale(H_index) + scale(idtnSim) + (scale(idtnSim) |      subID)
   Data: idSim2

REML criterion at convergence: 12709.5

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.6538 -0.6049 -0.2681  0.5208  6.9964 

Random effects:
 Groups   Name           Variance Std.Dev. Corr
 subID    (Intercept)    0.11399  0.3376       
          scale(idtnSim) 0.01178  0.1085   0.47
 Residual                0.59046  0.7684       
Number of obs: 5320, groups:  subID, 190

Fixed effects:
                 Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)     -0.008421   0.026967 181.508677  -0.312 0.755196    
scale(H_index)   0.491624   0.026904 183.417584  18.273  < 2e-16 ***
scale(idtnSim)   0.057631   0.015573 210.889610   3.701 0.000274 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) sc(H_)
scal(H_ndx)  0.021       
scal(dtnSm)  0.236 -0.094
m <-lmer(scale(idtpSim) ~ scale(SE) + scale(idtnSim) + ( scale(idtnSim)| subID), data=idSim2)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtpSim) ~ scale(SE) + scale(idtnSim) + (scale(idtnSim) |      subID)
   Data: idSim2

REML criterion at convergence: 12903.2

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.5909 -0.5992 -0.2714  0.5341  7.0284 

Random effects:
 Groups   Name           Variance Std.Dev. Corr
 subID    (Intercept)    0.36386  0.6032       
          scale(idtnSim) 0.01158  0.1076   0.41
 Residual                0.59027  0.7683       
Number of obs: 5320, groups:  subID, 190

Fixed effects:
                Estimate Std. Error        df t value Pr(>|t|)    
(Intercept)     -0.01148    0.04520 181.99825  -0.254 0.799708    
scale(SE)        0.04049    0.04413 162.70387   0.917 0.360315    
scale(idtnSim)   0.06088    0.01589 212.59468   3.830 0.000169 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) sc(SE)
scale(SE)    0.002       
scal(dtnSm)  0.201 -0.003
m <-lmer(scale(idtpSim) ~ scale(NFC) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim2)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtpSim) ~ scale(NFC) + scale(idtnSim) + (scale(idtnSim) |      subID)
   Data: idSim2

REML criterion at convergence: 12904

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.5866 -0.5979 -0.2681  0.5341  7.0213 

Random effects:
 Groups   Name           Variance Std.Dev. Corr
 subID    (Intercept)    0.36524  0.6043       
          scale(idtnSim) 0.01159  0.1076   0.39
 Residual                0.59022  0.7683       
Number of obs: 5320, groups:  subID, 190

Fixed effects:
                 Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)     -0.011245   0.045282 182.096123  -0.248 0.804155    
scale(NFC)      -0.009651   0.044190 162.331755  -0.218 0.827402    
scale(idtnSim)   0.060755   0.015902 212.257575   3.821 0.000175 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) s(NFC)
scale(NFC)  -0.004       
scal(dtnSm)  0.193  0.016
m <-lmer(scale(idtpSim) ~ scale(DS) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim2)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtpSim) ~ scale(DS) + scale(idtnSim) + (scale(idtnSim) |      subID)
   Data: idSim2

REML criterion at convergence: 12902

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.5839 -0.5988 -0.2765  0.5356  7.0065 

Random effects:
 Groups   Name           Variance Std.Dev. Corr
 subID    (Intercept)    0.36081  0.6007       
          scale(idtnSim) 0.01146  0.1070   0.38
 Residual                0.59022  0.7683       
Number of obs: 5320, groups:  subID, 190

Fixed effects:
                Estimate Std. Error        df t value Pr(>|t|)    
(Intercept)     -0.01097    0.04502 182.20703  -0.244 0.807757    
scale(DS)        0.06371    0.04429 168.57319   1.438 0.152197    
scale(idtnSim)   0.06096    0.01588 212.57124   3.839 0.000163 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) sc(DS)
scale(DS)   0.000        
scal(dtnSm) 0.184  0.002 
m <-lmer(scale(idtpSim) ~ scale(SCC) + scale(idtnSim) + ( scale(idtnSim)| subID), data=idSim2)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtpSim) ~ scale(SCC) + scale(idtnSim) + (scale(idtnSim) |      subID)
   Data: idSim2

REML criterion at convergence: 12904

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.5877 -0.5984 -0.2681  0.5335  7.0228 

Random effects:
 Groups   Name           Variance Std.Dev. Corr
 subID    (Intercept)    0.3652   0.6043       
          scale(idtnSim) 0.0116   0.1077   0.40
 Residual                0.5902   0.7683       
Number of obs: 5320, groups:  subID, 190

Fixed effects:
                Estimate Std. Error        df t value Pr(>|t|)    
(Intercept)     -0.01131    0.04528 182.04489  -0.250 0.803065    
scale(SCC)       0.01090    0.04434 165.33351   0.246 0.806200    
scale(idtnSim)   0.06080    0.01590 212.20307   3.824 0.000173 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) s(SCC)
scale(SCC)   0.002       
scal(dtnSm)  0.195 -0.005
m <-lmer(scale(idtpSim) ~ scale(MemSE) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim2)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtpSim) ~ scale(MemSE) + scale(idtnSim) + (scale(idtnSim) |      subID)
   Data: idSim2

REML criterion at convergence: 12901.2

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.5934 -0.6041 -0.2638  0.5359  7.0332 

Random effects:
 Groups   Name           Variance Std.Dev. Corr
 subID    (Intercept)    0.35978  0.5998       
          scale(idtnSim) 0.01165  0.1079   0.42
 Residual                0.59027  0.7683       
Number of obs: 5320, groups:  subID, 190

Fixed effects:
                Estimate Std. Error        df t value Pr(>|t|)    
(Intercept)     -0.01185    0.04496 181.91972  -0.264 0.792403    
scale(MemSE)     0.07461    0.04394 164.79246   1.698 0.091375 .  
scale(idtnSim)   0.06133    0.01590 212.37079   3.857 0.000152 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) s(MSE)
scale(MmSE) -0.001       
scal(dtnSm)  0.204  0.009
m <-lmer(scale(idtpSim) ~ scale(PrivCSE) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim2)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtpSim) ~ scale(PrivCSE) + scale(idtnSim) + (scale(idtnSim) |      subID)
   Data: idSim2

REML criterion at convergence: 12901.8

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.5894 -0.6011 -0.2681  0.5327  7.0283 

Random effects:
 Groups   Name           Variance Std.Dev. Corr
 subID    (Intercept)    0.36099  0.6008       
          scale(idtnSim) 0.01157  0.1076   0.40
 Residual                0.59025  0.7683       
Number of obs: 5320, groups:  subID, 190

Fixed effects:
                Estimate Std. Error        df t value Pr(>|t|)    
(Intercept)     -0.01168    0.04503 182.06825  -0.259 0.795566    
scale(PrivCSE)   0.06577    0.04401 164.50205   1.495 0.136956    
scale(idtnSim)   0.06133    0.01589 212.72319   3.859 0.000151 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) s(PCSE
scl(PrvCSE) -0.003       
scal(dtnSm)  0.197  0.014
m <-lmer(scale(idtpSim) ~ scale(PubCSE) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim2)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtpSim) ~ scale(PubCSE) + scale(idtnSim) + (scale(idtnSim) |      subID)
   Data: idSim2

REML criterion at convergence: 12904

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.5873 -0.5985 -0.2688  0.5339  7.0228 

Random effects:
 Groups   Name           Variance Std.Dev. Corr
 subID    (Intercept)    0.36516  0.6043       
          scale(idtnSim) 0.01157  0.1075   0.39
 Residual                0.59023  0.7683       
Number of obs: 5320, groups:  subID, 190

Fixed effects:
                 Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)     -0.011291   0.045277 182.038936  -0.249  0.80335    
scale(PubCSE)    0.009957   0.044193 163.020187   0.225  0.82201    
scale(idtnSim)   0.060840   0.015896 212.297480   3.827  0.00017 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) s(PCSE
scal(PbCSE) -0.001       
scal(dtnSm)  0.193  0.007
m <-lmer(scale(idtpSim) ~ scale(MemSE) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim2)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtpSim) ~ scale(MemSE) + scale(idtnSim) + (scale(idtnSim) |      subID)
   Data: idSim2

REML criterion at convergence: 12901.2

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.5934 -0.6041 -0.2638  0.5359  7.0332 

Random effects:
 Groups   Name           Variance Std.Dev. Corr
 subID    (Intercept)    0.35978  0.5998       
          scale(idtnSim) 0.01165  0.1079   0.42
 Residual                0.59027  0.7683       
Number of obs: 5320, groups:  subID, 190

Fixed effects:
                Estimate Std. Error        df t value Pr(>|t|)    
(Intercept)     -0.01185    0.04496 181.91972  -0.264 0.792403    
scale(MemSE)     0.07461    0.04394 164.79246   1.698 0.091375 .  
scale(idtnSim)   0.06133    0.01590 212.37079   3.857 0.000152 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) s(MSE)
scale(MmSE) -0.001       
scal(dtnSm)  0.204  0.009

Negative

m <-lmer(scale(idtnSim) ~ scale(SCC) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim2)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtnSim) ~ scale(SCC) + scale(idtpSim) + (scale(idtpSim) |      subID)
   Data: idSim2

REML criterion at convergence: 13051.1

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.3284 -0.6148 -0.2177  0.4557  3.8897 

Random effects:
 Groups   Name           Variance Std.Dev. Corr
 subID    (Intercept)    0.36388  0.6032       
          scale(idtpSim) 0.01224  0.1106   0.11
 Residual                0.60686  0.7790       
Number of obs: 5320, groups:  subID, 190

Fixed effects:
                 Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)     -0.006071   0.045264 186.259128  -0.134 0.893454    
scale(SCC)       0.009581   0.045205 185.460170   0.212 0.832387    
scale(idtpSim)   0.059056   0.016664 161.832472   3.544 0.000516 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) s(SCC)
scale(SCC)  0.000        
scal(dtpSm) 0.065  0.001 
m <-lmer(scale(idtnSim) ~ scale(Ind) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim2)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtnSim) ~ scale(Ind) + scale(idtpSim) + (scale(idtpSim) |      subID)
   Data: idSim2

REML criterion at convergence: 13050.9

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.3281 -0.6147 -0.2176  0.4553  3.8886 

Random effects:
 Groups   Name           Variance Std.Dev. Corr
 subID    (Intercept)    0.36354  0.6029       
          scale(idtpSim) 0.01226  0.1107   0.11
 Residual                0.60685  0.7790       
Number of obs: 5320, groups:  subID, 190

Fixed effects:
                 Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)     -0.006021   0.045245 186.258105  -0.133 0.894284    
scale(Ind)      -0.021601   0.045046 182.480469  -0.480 0.632141    
scale(idtpSim)   0.059136   0.016668 161.948869   3.548 0.000508 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) scl(I)
scale(Ind)  -0.001       
scal(dtpSm)  0.064 -0.007
m <-lmer(scale(idtnSim) ~ scale(Inter) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim2)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtnSim) ~ scale(Inter) + scale(idtpSim) + (scale(idtpSim) |      subID)
   Data: idSim2

REML criterion at convergence: 13050.7

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.3282 -0.6134 -0.2173  0.4568  3.8883 

Random effects:
 Groups   Name           Variance Std.Dev. Corr
 subID    (Intercept)    0.36332  0.6028       
          scale(idtpSim) 0.01208  0.1099   0.12
 Residual                0.60692  0.7791       
Number of obs: 5320, groups:  subID, 190

Fixed effects:
                 Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)     -0.006016   0.045230 186.341070  -0.133 0.894328    
scale(Inter)     0.028702   0.045152 185.262560   0.636 0.525764    
scale(idtpSim)   0.058990   0.016636 161.914976   3.546 0.000512 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) scl(I)
scale(Intr)  0.002       
scal(dtpSm)  0.066 -0.008
m <-lmer(scale(idtnSim) ~ scale(SWLS) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim2)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtnSim) ~ scale(SWLS) + scale(idtpSim) + (scale(idtpSim) |      subID)
   Data: idSim2

REML criterion at convergence: 13050.2

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.3290 -0.6164 -0.2180  0.4557  3.8944 

Random effects:
 Groups   Name           Variance Std.Dev. Corr
 subID    (Intercept)    0.3621   0.6018       
          scale(idtpSim) 0.0122   0.1104   0.12
 Residual                0.6069   0.7790       
Number of obs: 5320, groups:  subID, 190

Fixed effects:
                 Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)     -0.006285   0.045160 186.222869  -0.139 0.889473    
scale(SWLS)      0.043654   0.044985 183.530958   0.970 0.333115    
scale(idtpSim)   0.059118   0.016657 161.486547   3.549 0.000506 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) s(SWLS
scale(SWLS) -0.002       
scal(dtpSm)  0.069  0.004
m <-lmer(scale(idtnSim) ~ scale(IdImp) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim2)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtnSim) ~ scale(IdImp) + scale(idtpSim) + (scale(idtpSim) |      subID)
   Data: idSim2

REML criterion at convergence: 13051.1

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.3279 -0.6123 -0.2176  0.4560  3.8895 

Random effects:
 Groups   Name           Variance Std.Dev. Corr
 subID    (Intercept)    0.36381  0.6032       
          scale(idtpSim) 0.01225  0.1107   0.11
 Residual                0.60685  0.7790       
Number of obs: 5320, groups:  subID, 190

Fixed effects:
                 Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)     -0.005996   0.045261 186.285829  -0.132 0.894759    
scale(IdImp)     0.012148   0.045184 185.152606   0.269 0.788342    
scale(idtpSim)   0.058989   0.016670 161.883722   3.539 0.000525 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) sc(II)
scal(IdImp)  0.002       
scal(dtpSm)  0.063 -0.021
m <-lmer(scale(idtnSim) ~ scale(phi) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim2)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtnSim) ~ scale(phi) + scale(idtpSim) + (scale(idtpSim) |      subID)
   Data: idSim2

REML criterion at convergence: 13046.8

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.3227 -0.6125 -0.2181  0.4494  3.8938 

Random effects:
 Groups   Name           Variance Std.Dev. Corr
 subID    (Intercept)    0.35525  0.5960       
          scale(idtpSim) 0.01224  0.1106   0.07
 Residual                0.60680  0.7790       
Number of obs: 5320, groups:  subID, 190

Fixed effects:
                 Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)     -0.004952   0.044763 186.655064  -0.111 0.912037    
scale(phi)      -0.094360   0.044716 184.253757  -2.110 0.036189 *  
scale(idtpSim)   0.058446   0.016662 162.781184   3.508 0.000584 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) scl(p)
scale(phi)  -0.004       
scal(dtpSm)  0.043  0.036
m <-lmer(scale(idtnSim) ~ scale(overlap_norm) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim2)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtnSim) ~ scale(overlap_norm) + scale(idtpSim) + (scale(idtpSim) |      subID)
   Data: idSim2

REML criterion at convergence: 13032.1

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.3098 -0.6118 -0.2228  0.4504  3.8960 

Random effects:
 Groups   Name           Variance Std.Dev. Corr
 subID    (Intercept)    0.32989  0.5744       
          scale(idtpSim) 0.01175  0.1084   0.04
 Residual                0.60680  0.7790       
Number of obs: 5320, groups:  subID, 190

Fixed effects:
                      Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)          -0.001836   0.043245 189.524045  -0.042  0.96619    
scale(overlap_norm)   0.201559   0.045126 205.503201   4.467 1.31e-05 ***
scale(idtpSim)        0.048314   0.016774 170.989681   2.880  0.00448 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) scl(_)
scl(vrlp_n)  0.016       
scal(dtpSm)  0.029 -0.169
m <-lmer(scale(idtnSim) ~ scale(H_index) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim2)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtnSim) ~ scale(H_index) + scale(idtpSim) + (scale(idtpSim) |      subID)
   Data: idSim2

REML criterion at convergence: 13038

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.3152 -0.6161 -0.2175  0.4545  3.8975 

Random effects:
 Groups   Name           Variance Std.Dev. Corr
 subID    (Intercept)    0.34037  0.5834       
          scale(idtpSim) 0.01162  0.1078   0.11
 Residual                0.60696  0.7791       
Number of obs: 5320, groups:  subID, 190

Fixed effects:
                 Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)     -0.003433   0.043873 188.517595  -0.078 0.937706    
scale(H_index)   0.165783   0.045210 202.285027   3.667 0.000314 ***
scale(idtpSim)   0.051133   0.016708 168.862963   3.060 0.002573 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) sc(H_)
scal(H_ndx)  0.016       
scal(dtpSm)  0.058 -0.145
m <-lmer(scale(idtnSim) ~ scale(SE) + scale(idtpSim) + ( scale(idtpSim)| subID), data=idSim2)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtnSim) ~ scale(SE) + scale(idtpSim) + (scale(idtpSim) |      subID)
   Data: idSim2

REML criterion at convergence: 13051.1

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.3284 -0.6138 -0.2182  0.4557  3.8922 

Random effects:
 Groups   Name           Variance Std.Dev. Corr
 subID    (Intercept)    0.36384  0.6032       
          scale(idtpSim) 0.01223  0.1106   0.11
 Residual                0.60687  0.7790       
Number of obs: 5320, groups:  subID, 190

Fixed effects:
                 Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)     -0.006086   0.045262 186.250214  -0.134 0.893184    
scale(SE)        0.012480   0.045097 183.418709   0.277 0.782293    
scale(idtpSim)   0.059006   0.016664 161.789669   3.541 0.000521 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) sc(SE)
scale(SE)    0.000       
scal(dtpSm)  0.065 -0.009
m <-lmer(scale(idtnSim) ~ scale(NFC) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim2)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtnSim) ~ scale(NFC) + scale(idtpSim) + (scale(idtpSim) |      subID)
   Data: idSim2

REML criterion at convergence: 13050

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.3281 -0.6143 -0.2208  0.4536  3.8910 

Random effects:
 Groups   Name           Variance Std.Dev. Corr
 subID    (Intercept)    0.36154  0.6013       
          scale(idtpSim) 0.01217  0.1103   0.11
 Residual                0.60689  0.7790       
Number of obs: 5320, groups:  subID, 190

Fixed effects:
                 Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)     -0.006008   0.045128 186.219145  -0.133 0.894224    
scale(NFC)      -0.048851   0.045025 184.855552  -1.085 0.279349    
scale(idtpSim)   0.059117   0.016650 162.074632   3.551 0.000503 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) s(NFC)
scale(NFC)  -0.001       
scal(dtpSm)  0.064  0.002
m <-lmer(scale(idtnSim) ~ scale(DS) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim2)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtnSim) ~ scale(DS) + scale(idtpSim) + (scale(idtpSim) |      subID)
   Data: idSim2

REML criterion at convergence: 13051.1

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.3282 -0.6146 -0.2179  0.4561  3.8894 

Random effects:
 Groups   Name           Variance Std.Dev. Corr
 subID    (Intercept)    0.36393  0.6033       
          scale(idtpSim) 0.01224  0.1107   0.11
 Residual                0.60686  0.7790       
Number of obs: 5320, groups:  subID, 190

Fixed effects:
                 Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)     -0.006036   0.045268 186.269226  -0.133 0.894072    
scale(DS)        0.002183   0.045459 188.853591   0.048 0.961746    
scale(idtpSim)   0.059050   0.016668 161.751701   3.543 0.000518 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) sc(DS)
scale(DS)    0.004       
scal(dtpSm)  0.064 -0.019
m <-lmer(scale(idtnSim) ~ scale(SCC) + scale(idtpSim) + ( scale(idtpSim)| subID), data=idSim2)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtnSim) ~ scale(SCC) + scale(idtpSim) + (scale(idtpSim) |      subID)
   Data: idSim2

REML criterion at convergence: 13051.1

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.3284 -0.6148 -0.2177  0.4557  3.8897 

Random effects:
 Groups   Name           Variance Std.Dev. Corr
 subID    (Intercept)    0.36388  0.6032       
          scale(idtpSim) 0.01224  0.1106   0.11
 Residual                0.60686  0.7790       
Number of obs: 5320, groups:  subID, 190

Fixed effects:
                 Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)     -0.006071   0.045264 186.259128  -0.134 0.893454    
scale(SCC)       0.009581   0.045205 185.460170   0.212 0.832387    
scale(idtpSim)   0.059056   0.016664 161.832472   3.544 0.000516 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) s(SCC)
scale(SCC)  0.000        
scal(dtpSm) 0.065  0.001 
m <-lmer(scale(idtnSim) ~ scale(MemSE) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim2)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtnSim) ~ scale(MemSE) + scale(idtpSim) + (scale(idtpSim) |      subID)
   Data: idSim2

REML criterion at convergence: 13050.7

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.3286 -0.6140 -0.2177  0.4553  3.8883 

Random effects:
 Groups   Name           Variance Std.Dev. Corr
 subID    (Intercept)    0.36289  0.6024       
          scale(idtpSim) 0.01228  0.1108   0.11
 Residual                0.60685  0.7790       
Number of obs: 5320, groups:  subID, 190

Fixed effects:
                 Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)     -0.006015   0.045208 186.140779  -0.133 0.894297    
scale(MemSE)    -0.029083   0.045189 185.880184  -0.644 0.520637    
scale(idtpSim)   0.059346   0.016674 161.957173   3.559 0.000488 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) s(MSE)
scale(MmSE)  0.001       
scal(dtpSm)  0.062 -0.020
m <-lmer(scale(idtnSim) ~ scale(PrivCSE) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim2)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtnSim) ~ scale(PrivCSE) + scale(idtpSim) + (scale(idtpSim) |      subID)
   Data: idSim2

REML criterion at convergence: 13049.4

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.3297 -0.6113 -0.2214  0.4541  3.8932 

Random effects:
 Groups   Name           Variance Std.Dev. Corr
 subID    (Intercept)    0.35997  0.6000       
          scale(idtpSim) 0.01227  0.1108   0.11
 Residual                0.60687  0.7790       
Number of obs: 5320, groups:  subID, 190

Fixed effects:
                 Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)     -0.006224   0.045037 185.940696  -0.138  0.89024    
scale(PrivCSE)  -0.059369   0.045057 186.373221  -1.318  0.18925    
scale(idtpSim)   0.059620   0.016671 162.342225   3.576  0.00046 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) s(PCSE
scl(PrvCSE)  0.002       
scal(dtpSm)  0.065 -0.020
m <-lmer(scale(idtnSim) ~ scale(PubCSE) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim2)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtnSim) ~ scale(PubCSE) + scale(idtpSim) + (scale(idtpSim) |      subID)
   Data: idSim2

REML criterion at convergence: 13051.1

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.3282 -0.6156 -0.2174  0.4556  3.8888 

Random effects:
 Groups   Name           Variance Std.Dev. Corr
 subID    (Intercept)    0.36380  0.6032       
          scale(idtpSim) 0.01224  0.1106   0.11
 Residual                0.60686  0.7790       
Number of obs: 5320, groups:  subID, 190

Fixed effects:
                 Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)     -0.006065   0.045260 186.240784  -0.134 0.893540    
scale(PubCSE)   -0.012395   0.045171 184.882593  -0.274 0.784074    
scale(idtpSim)   0.059080   0.016665 161.905271   3.545 0.000513 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) s(PCSE
scal(PbCSE) -0.001       
scal(dtpSm)  0.065 -0.006
m <-lmer(scale(idtnSim) ~ scale(MemSE) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim2)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtnSim) ~ scale(MemSE) + scale(idtpSim) + (scale(idtpSim) |      subID)
   Data: idSim2

REML criterion at convergence: 13050.7

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.3286 -0.6140 -0.2177  0.4553  3.8883 

Random effects:
 Groups   Name           Variance Std.Dev. Corr
 subID    (Intercept)    0.36289  0.6024       
          scale(idtpSim) 0.01228  0.1108   0.11
 Residual                0.60685  0.7790       
Number of obs: 5320, groups:  subID, 190

Fixed effects:
                 Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)     -0.006015   0.045208 186.140779  -0.133 0.894297    
scale(MemSE)    -0.029083   0.045189 185.880184  -0.644 0.520637    
scale(idtpSim)   0.059346   0.016674 161.957173   3.559 0.000488 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) s(MSE)
scale(MmSE)  0.001       
scal(dtpSm)  0.062 -0.020

Both

m <-lmer(scale(idtSim) ~ scale(SCC) + ( 1 | subID), data=idSim2)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtSim) ~ scale(SCC) + (1 | subID)
   Data: idSim2

REML criterion at convergence: 12653

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.6156 -0.6504 -0.2404  0.5367  8.3469 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.4405   0.6637  
 Residual             0.5641   0.7511  
Number of obs: 5320, groups:  subID, 190

Fixed effects:
              Estimate Std. Error         df t value Pr(>|t|)
(Intercept) -8.732e-15  4.924e-02  1.880e+02   0.000    1.000
scale(SCC)   5.967e-03  4.924e-02  1.880e+02   0.121    0.904

Correlation of Fixed Effects:
           (Intr)
scale(SCC) 0.000 
m <-lmer(scale(idtSim) ~ scale(Ind) + ( 1 | subID), data=idSim2)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtSim) ~ scale(Ind) + (1 | subID)
   Data: idSim2

REML criterion at convergence: 12652.9

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.6156 -0.6509 -0.2403  0.5375  8.3481 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.4402   0.6635  
 Residual             0.5641   0.7511  
Number of obs: 5320, groups:  subID, 190

Fixed effects:
              Estimate Std. Error         df t value Pr(>|t|)
(Intercept) -6.542e-15  4.922e-02  1.880e+02    0.00    1.000
scale(Ind)   1.721e-02  4.923e-02  1.880e+02    0.35    0.727

Correlation of Fixed Effects:
           (Intr)
scale(Ind) 0.000 
m <-lmer(scale(idtSim) ~ scale(Inter) + ( 1 | subID), data=idSim2)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtSim) ~ scale(Inter) + (1 | subID)
   Data: idSim2

REML criterion at convergence: 12652.7

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.6161 -0.6487 -0.2408  0.5375  8.3471 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.4398   0.6631  
 Residual             0.5641   0.7511  
Number of obs: 5320, groups:  subID, 190

Fixed effects:
               Estimate Std. Error         df t value Pr(>|t|)
(Intercept)  -7.859e-15  4.920e-02  1.880e+02   0.000    1.000
scale(Inter)  2.766e-02  4.920e-02  1.880e+02   0.562    0.575

Correlation of Fixed Effects:
            (Intr)
scale(Intr) 0.000 
m <-lmer(scale(idtSim) ~ scale(SWLS) + ( 1 | subID), data=idSim2)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtSim) ~ scale(SWLS) + (1 | subID)
   Data: idSim2

REML criterion at convergence: 12652.6

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.6155 -0.6493 -0.2389  0.5372  8.3423 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.4395   0.6629  
 Residual             0.5641   0.7511  
Number of obs: 5320, groups:  subID, 190

Fixed effects:
              Estimate Std. Error         df t value Pr(>|t|)
(Intercept) -7.363e-15  4.918e-02  1.880e+02   0.000    1.000
scale(SWLS) -3.235e-02  4.919e-02  1.880e+02  -0.658    0.512

Correlation of Fixed Effects:
            (Intr)
scale(SWLS) 0.000 
m <-lmer(scale(idtSim) ~ scale(IdImp) + ( 1 | subID), data=idSim2)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtSim) ~ scale(IdImp) + (1 | subID)
   Data: idSim2

REML criterion at convergence: 12651.3

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.6133 -0.6468 -0.2401  0.5377  8.3479 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.4365   0.6607  
 Residual             0.5641   0.7511  
Number of obs: 5320, groups:  subID, 190

Fixed effects:
               Estimate Std. Error         df t value Pr(>|t|)
(Intercept)  -3.422e-15  4.902e-02  1.880e+02   0.000    1.000
scale(IdImp)  6.342e-02  4.903e-02  1.880e+02   1.293    0.197

Correlation of Fixed Effects:
            (Intr)
scal(IdImp) 0.000 
m <-lmer(scale(idtSim) ~ scale(phi) + ( 1 | subID), data=idSim2)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtSim) ~ scale(phi) + (1 | subID)
   Data: idSim2

REML criterion at convergence: 12648.9

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.6191 -0.6523 -0.2411  0.5402  8.3446 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.4307   0.6562  
 Residual             0.5641   0.7511  
Number of obs: 5320, groups:  subID, 190

Fixed effects:
              Estimate Std. Error         df t value Pr(>|t|)  
(Intercept) -4.788e-15  4.871e-02  1.880e+02   0.000   1.0000  
scale(phi)  -9.884e-02  4.871e-02  1.880e+02  -2.029   0.0439 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
           (Intr)
scale(phi) 0.000 
m <-lmer(scale(idtSim) ~ scale(overlap_norm) + ( 1 | subID), data=idSim2)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtSim) ~ scale(overlap_norm) + (1 | subID)
   Data: idSim2

REML criterion at convergence: 12149.8

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.8477 -0.6403 -0.2032  0.5488  8.4500 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.01154  0.1074  
 Residual             0.56413  0.7511  
Number of obs: 5320, groups:  subID, 190

Fixed effects:
                      Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)         -3.037e-16  1.291e-02  1.880e+02    0.00        1    
scale(overlap_norm)  6.516e-01  1.292e-02  1.880e+02   50.45   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr)
scl(vrlp_n) 0.000 
m <-lmer(scale(idtSim) ~ scale(H_index) + ( 1 | subID), data=idSim2)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtSim) ~ scale(H_index) + (1 | subID)
   Data: idSim2

REML criterion at convergence: 12467.3

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.7557 -0.6438 -0.2423  0.5303  8.4208 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.1514   0.3891  
 Residual             0.5641   0.7511  
Number of obs: 5320, groups:  subID, 190

Fixed effects:
                Estimate Std. Error        df t value Pr(>|t|)    
(Intercept)    6.909e-16  3.005e-02 1.880e+02     0.0        1    
scale(H_index) 5.349e-01  3.005e-02 1.880e+02    17.8   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr)
scal(H_ndx) 0.000 
m <-lmer(scale(idtSim) ~ scale(SE) + ( 1| subID), data=idSim2)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtSim) ~ scale(SE) + (1 | subID)
   Data: idSim2

REML criterion at convergence: 12652.7

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.6147 -0.6504 -0.2409  0.5369  8.3491 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.4398   0.6632  
 Residual             0.5641   0.7511  
Number of obs: 5320, groups:  subID, 190

Fixed effects:
              Estimate Std. Error         df t value Pr(>|t|)
(Intercept) -8.139e-15  4.920e-02  1.880e+02   0.000    1.000
scale(SE)    2.739e-02  4.920e-02  1.880e+02   0.557    0.578

Correlation of Fixed Effects:
          (Intr)
scale(SE) 0.000 
m <-lmer(scale(idtSim) ~ scale(NFC) + ( 1 | subID), data=idSim2)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtSim) ~ scale(NFC) + (1 | subID)
   Data: idSim2

REML criterion at convergence: 12653

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.6159 -0.6502 -0.2401  0.5367  8.3471 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.4405   0.6637  
 Residual             0.5641   0.7511  
Number of obs: 5320, groups:  subID, 190

Fixed effects:
              Estimate Std. Error         df t value Pr(>|t|)
(Intercept) -8.703e-15  4.924e-02  1.880e+02   0.000    1.000
scale(NFC)  -6.908e-04  4.924e-02  1.880e+02  -0.014    0.989

Correlation of Fixed Effects:
           (Intr)
scale(NFC) 0.000 
m <-lmer(scale(idtSim) ~ scale(DS) + ( 1 | subID), data=idSim2)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtSim) ~ scale(DS) + (1 | subID)
   Data: idSim2

REML criterion at convergence: 12649.9

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.6132 -0.6473 -0.2407  0.5361  8.3423 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.4331   0.6581  
 Residual             0.5641   0.7511  
Number of obs: 5320, groups:  subID, 190

Fixed effects:
              Estimate Std. Error         df t value Pr(>|t|)  
(Intercept) -7.762e-15  4.884e-02  1.880e+02   0.000   1.0000  
scale(DS)    8.562e-02  4.885e-02  1.880e+02   1.753   0.0813 .
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
          (Intr)
scale(DS) 0.000 
m <-lmer(scale(idtSim) ~ scale(SCC) + ( 1| subID), data=idSim2)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtSim) ~ scale(SCC) + (1 | subID)
   Data: idSim2

REML criterion at convergence: 12653

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.6156 -0.6504 -0.2404  0.5367  8.3469 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.4405   0.6637  
 Residual             0.5641   0.7511  
Number of obs: 5320, groups:  subID, 190

Fixed effects:
              Estimate Std. Error         df t value Pr(>|t|)
(Intercept) -8.732e-15  4.924e-02  1.880e+02   0.000    1.000
scale(SCC)   5.967e-03  4.924e-02  1.880e+02   0.121    0.904

Correlation of Fixed Effects:
           (Intr)
scale(SCC) 0.000 
m <-lmer(scale(idtSim) ~ scale(MemSE) + ( 1 | subID), data=idSim2)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtSim) ~ scale(MemSE) + (1 | subID)
   Data: idSim2

REML criterion at convergence: 12651.9

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.6140 -0.6489 -0.2407  0.5372  8.3493 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.4378   0.6617  
 Residual             0.5641   0.7511  
Number of obs: 5320, groups:  subID, 190

Fixed effects:
               Estimate Std. Error         df t value Pr(>|t|)
(Intercept)  -8.152e-15  4.910e-02  1.880e+02   0.000    1.000
scale(MemSE)  5.180e-02  4.910e-02  1.880e+02   1.055    0.293

Correlation of Fixed Effects:
            (Intr)
scale(MmSE) 0.000 
m <-lmer(scale(idtSim) ~ scale(PrivCSE) + ( 1 | subID), data=idSim2)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtSim) ~ scale(PrivCSE) + (1 | subID)
   Data: idSim2

REML criterion at convergence: 12650.4

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.6141 -0.6520 -0.2417  0.5372  8.3514 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.4343   0.6590  
 Residual             0.5641   0.7511  
Number of obs: 5320, groups:  subID, 190

Fixed effects:
                 Estimate Std. Error         df t value Pr(>|t|)
(Intercept)    -8.433e-15  4.891e-02  1.880e+02   0.000     1.00
scale(PrivCSE)  7.850e-02  4.891e-02  1.880e+02   1.605     0.11

Correlation of Fixed Effects:
            (Intr)
scl(PrvCSE) 0.000 
m <-lmer(scale(idtSim) ~ scale(PubCSE) + ( 1 | subID), data=idSim2)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtSim) ~ scale(PubCSE) + (1 | subID)
   Data: idSim2

REML criterion at convergence: 12653

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.6155 -0.6501 -0.2406  0.5369  8.3474 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.4405   0.6637  
 Residual             0.5641   0.7511  
Number of obs: 5320, groups:  subID, 190

Fixed effects:
                Estimate Std. Error         df t value Pr(>|t|)
(Intercept)   -9.156e-15  4.924e-02  1.880e+02   0.000    1.000
scale(PubCSE)  7.534e-03  4.924e-02  1.880e+02   0.153    0.879

Correlation of Fixed Effects:
            (Intr)
scal(PbCSE) 0.000 
m <-lmer(scale(idtSim) ~ scale(MemSE) + ( 1 | subID), data=idSim2)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idtSim) ~ scale(MemSE) + (1 | subID)
   Data: idSim2

REML criterion at convergence: 12651.9

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.6140 -0.6489 -0.2407  0.5372  8.3493 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.4378   0.6617  
 Residual             0.5641   0.7511  
Number of obs: 5320, groups:  subID, 190

Fixed effects:
               Estimate Std. Error         df t value Pr(>|t|)
(Intercept)  -8.152e-15  4.910e-02  1.880e+02   0.000    1.000
scale(MemSE)  5.180e-02  4.910e-02  1.880e+02   1.055    0.293

Correlation of Fixed Effects:
            (Intr)
scale(MmSE) 0.000 

Individual Differences Predict Pairwise Overlap in Identities in Common

m <-lmer(scale(idSim) ~ scale(SCC) + ( 1 | subID), data=idSim2)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idSim) ~ scale(SCC) + (1 | subID)
   Data: idSim2

REML criterion at convergence: 13077.8

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.3269 -0.5946 -0.2387  0.4702  3.8500 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.3886   0.6234  
 Residual             0.6154   0.7845  
Number of obs: 5320, groups:  subID, 190

Fixed effects:
              Estimate Std. Error         df t value Pr(>|t|)
(Intercept) -5.653e-15  4.649e-02  1.880e+02   0.000    1.000
scale(SCC)   9.345e-03  4.649e-02  1.880e+02   0.201    0.841

Correlation of Fixed Effects:
           (Intr)
scale(SCC) 0.000 
m <-lmer(scale(idSim) ~ scale(Ind) + ( 1 | subID), data=idSim2)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idSim) ~ scale(Ind) + (1 | subID)
   Data: idSim2

REML criterion at convergence: 13077.7

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.3256 -0.5957 -0.2379  0.4697  3.8522 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.3883   0.6231  
 Residual             0.6154   0.7845  
Number of obs: 5320, groups:  subID, 190

Fixed effects:
              Estimate Std. Error         df t value Pr(>|t|)
(Intercept) -4.541e-15  4.647e-02  1.880e+02   0.000    1.000
scale(Ind)  -2.007e-02  4.647e-02  1.880e+02  -0.432    0.666

Correlation of Fixed Effects:
           (Intr)
scale(Ind) 0.000 
m <-lmer(scale(idSim) ~ scale(Inter) + ( 1 | subID), data=idSim2)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idSim) ~ scale(Inter) + (1 | subID)
   Data: idSim2

REML criterion at convergence: 13077.2

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.3318 -0.5988 -0.2342  0.4684  3.8557 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.3873   0.6224  
 Residual             0.6154   0.7845  
Number of obs: 5320, groups:  subID, 190

Fixed effects:
               Estimate Std. Error         df t value Pr(>|t|)
(Intercept)  -7.377e-15  4.641e-02  1.880e+02   0.000    1.000
scale(Inter)  3.678e-02  4.642e-02  1.880e+02   0.792    0.429

Correlation of Fixed Effects:
            (Intr)
scale(Intr) 0.000 
m <-lmer(scale(idSim) ~ scale(SWLS) + ( 1 | subID), data=idSim2)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idSim) ~ scale(SWLS) + (1 | subID)
   Data: idSim2

REML criterion at convergence: 13077.1

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.3263 -0.5977 -0.2349  0.4672  3.8540 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.3871   0.6222  
 Residual             0.6154   0.7845  
Number of obs: 5320, groups:  subID, 190

Fixed effects:
              Estimate Std. Error         df t value Pr(>|t|)
(Intercept) -2.995e-15  4.640e-02  1.880e+02   0.000    1.000
scale(SWLS)  3.974e-02  4.641e-02  1.880e+02   0.856    0.393

Correlation of Fixed Effects:
            (Intr)
scale(SWLS) 0.000 
m <-lmer(scale(idSim) ~ scale(IdImp) + ( 1 | subID), data=idSim2)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idSim) ~ scale(IdImp) + (1 | subID)
   Data: idSim2

REML criterion at convergence: 13077.7

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.3256 -0.5961 -0.2386  0.4705  3.8518 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.3884   0.6232  
 Residual             0.6154   0.7845  
Number of obs: 5320, groups:  subID, 190

Fixed effects:
               Estimate Std. Error         df t value Pr(>|t|)
(Intercept)  -7.121e-15  4.647e-02  1.880e+02   0.000    1.000
scale(IdImp)  1.827e-02  4.648e-02  1.880e+02   0.393    0.695

Correlation of Fixed Effects:
            (Intr)
scal(IdImp) 0.000 
m <-lmer(scale(idSim) ~ scale(phi) + ( 1 | subID), data=idSim2)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idSim) ~ scale(phi) + (1 | subID)
   Data: idSim2

REML criterion at convergence: 13072.5

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.3258 -0.6028 -0.2348  0.4654  3.8616 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.3771   0.6141  
 Residual             0.6154   0.7845  
Number of obs: 5320, groups:  subID, 190

Fixed effects:
              Estimate Std. Error         df t value Pr(>|t|)  
(Intercept) -6.439e-15  4.583e-02  1.880e+02    0.00   1.0000  
scale(phi)  -1.073e-01  4.583e-02  1.880e+02   -2.34   0.0203 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
           (Intr)
scale(phi) 0.000 
m <-lmer(scale(idSim) ~ scale(overlap_norm) + ( 1 | subID), data=idSim2)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idSim) ~ scale(overlap_norm) + (1 | subID)
   Data: idSim2

REML criterion at convergence: 13050.9

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.3427 -0.6071 -0.2309  0.4698  3.8628 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.3339   0.5778  
 Residual             0.6154   0.7845  
Number of obs: 5320, groups:  subID, 190

Fixed effects:
                      Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)         -2.790e-15  4.328e-02  1.880e+02   0.000        1    
scale(overlap_norm)  2.329e-01  4.328e-02  1.880e+02   5.381 2.19e-07 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr)
scl(vrlp_n) 0.000 
m <-lmer(scale(idSim) ~ scale(H_index) + ( 1 | subID), data=idSim2)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idSim) ~ scale(H_index) + (1 | subID)
   Data: idSim2

REML criterion at convergence: 13058.9

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.3461 -0.6160 -0.2261  0.4779  3.8683 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.3494   0.5911  
 Residual             0.6154   0.7845  
Number of obs: 5320, groups:  subID, 190

Fixed effects:
                 Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)    -2.634e-15  4.421e-02  1.880e+02    0.00        1    
scale(H_index)  1.972e-01  4.422e-02  1.880e+02    4.46 1.41e-05 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr)
scal(H_ndx) 0.000 
m <-lmer(scale(idSim) ~ scale(SE) + ( 1| subID), data=idSim2)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idSim) ~ scale(SE) + (1 | subID)
   Data: idSim2

REML criterion at convergence: 13077.8

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.3270 -0.5958 -0.2379  0.4697  3.8522 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.3886   0.6233  
 Residual             0.6154   0.7845  
Number of obs: 5320, groups:  subID, 190

Fixed effects:
              Estimate Std. Error         df t value Pr(>|t|)
(Intercept) -3.598e-15  4.648e-02  1.880e+02   0.000    1.000
scale(SE)    1.233e-02  4.649e-02  1.880e+02   0.265    0.791

Correlation of Fixed Effects:
          (Intr)
scale(SE) 0.000 
m <-lmer(scale(idSim) ~ scale(NFC) + ( 1 | subID), data=idSim2)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idSim) ~ scale(NFC) + (1 | subID)
   Data: idSim2

REML criterion at convergence: 13076.6

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.3295 -0.5963 -0.2367  0.4688  3.8576 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.3860   0.6213  
 Residual             0.6154   0.7845  
Number of obs: 5320, groups:  subID, 190

Fixed effects:
              Estimate Std. Error         df t value Pr(>|t|)
(Intercept) -4.977e-15  4.634e-02  1.880e+02   0.000    1.000
scale(NFC)  -5.217e-02  4.634e-02  1.880e+02  -1.126    0.262

Correlation of Fixed Effects:
           (Intr)
scale(NFC) 0.000 
m <-lmer(scale(idSim) ~ scale(DS) + ( 1 | subID), data=idSim2)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idSim) ~ scale(DS) + (1 | subID)
   Data: idSim2

REML criterion at convergence: 13077.7

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.3267 -0.5950 -0.2378  0.4711  3.8518 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.3885   0.6233  
 Residual             0.6154   0.7845  
Number of obs: 5320, groups:  subID, 190

Fixed effects:
              Estimate Std. Error         df t value Pr(>|t|)
(Intercept) -4.254e-15  4.648e-02  1.880e+02   0.000    1.000
scale(DS)    1.531e-02  4.648e-02  1.880e+02   0.329    0.742

Correlation of Fixed Effects:
          (Intr)
scale(DS) 0.000 
m <-lmer(scale(idSim) ~ scale(SCC) + ( 1| subID), data=idSim2)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idSim) ~ scale(SCC) + (1 | subID)
   Data: idSim2

REML criterion at convergence: 13077.8

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.3269 -0.5946 -0.2387  0.4702  3.8500 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.3886   0.6234  
 Residual             0.6154   0.7845  
Number of obs: 5320, groups:  subID, 190

Fixed effects:
              Estimate Std. Error         df t value Pr(>|t|)
(Intercept) -5.653e-15  4.649e-02  1.880e+02   0.000    1.000
scale(SCC)   9.345e-03  4.649e-02  1.880e+02   0.201    0.841

Correlation of Fixed Effects:
           (Intr)
scale(SCC) 0.000 
m <-lmer(scale(idSim) ~ scale(MemSE) + ( 1 | subID), data=idSim2)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idSim) ~ scale(MemSE) + (1 | subID)
   Data: idSim2

REML criterion at convergence: 13077.6

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.3261 -0.5965 -0.2374  0.4684  3.8533 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.3881   0.6229  
 Residual             0.6154   0.7845  
Number of obs: 5320, groups:  subID, 190

Fixed effects:
               Estimate Std. Error         df t value Pr(>|t|)
(Intercept)  -6.955e-15  4.646e-02  1.880e+02   0.000    1.000
scale(MemSE) -2.533e-02  4.646e-02  1.880e+02  -0.545    0.586

Correlation of Fixed Effects:
            (Intr)
scale(MmSE) 0.000 
m <-lmer(scale(idSim) ~ scale(PrivCSE) + ( 1 | subID), data=idSim2)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idSim) ~ scale(PrivCSE) + (1 | subID)
   Data: idSim2

REML criterion at convergence: 13076.5

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.3289 -0.5986 -0.2373  0.4683  3.8576 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.3857   0.6211  
 Residual             0.6154   0.7845  
Number of obs: 5320, groups:  subID, 190

Fixed effects:
                 Estimate Std. Error         df t value Pr(>|t|)
(Intercept)    -8.272e-15  4.632e-02  1.880e+02    0.00    1.000
scale(PrivCSE) -5.422e-02  4.633e-02  1.880e+02   -1.17    0.243

Correlation of Fixed Effects:
            (Intr)
scl(PrvCSE) 0.000 
m <-lmer(scale(idSim) ~ scale(PubCSE) + ( 1 | subID), data=idSim2)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idSim) ~ scale(PubCSE) + (1 | subID)
   Data: idSim2

REML criterion at convergence: 13077.8

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.3265 -0.5952 -0.2387  0.4698  3.8517 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.3886   0.6234  
 Residual             0.6154   0.7845  
Number of obs: 5320, groups:  subID, 190

Fixed effects:
                Estimate Std. Error         df t value Pr(>|t|)
(Intercept)   -5.276e-15  4.649e-02  1.880e+02   0.000    1.000
scale(PubCSE) -1.082e-02  4.649e-02  1.880e+02  -0.233    0.816

Correlation of Fixed Effects:
            (Intr)
scal(PbCSE) 0.000 
m <-lmer(scale(idSim) ~ scale(MemSE) + ( 1 | subID), data=idSim2)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(idSim) ~ scale(MemSE) + (1 | subID)
   Data: idSim2

REML criterion at convergence: 13077.6

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.3261 -0.5965 -0.2374  0.4684  3.8533 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.3881   0.6229  
 Residual             0.6154   0.7845  
Number of obs: 5320, groups:  subID, 190

Fixed effects:
               Estimate Std. Error         df t value Pr(>|t|)
(Intercept)  -6.955e-15  4.646e-02  1.880e+02   0.000    1.000
scale(MemSE) -2.533e-02  4.646e-02  1.880e+02  -0.545    0.586

Correlation of Fixed Effects:
            (Intr)
scale(MmSE) 0.000 

More trait overlap predicts more intergroup bias

Study 2 Only

m<-lmer(scale(interG) ~ scale(traitCommNod) + scale(idCommNod) + ( scale(traitCommNod) + scale(idCommNod) | subID) + ( 1  | id), data=idShort2)
summary(m)

Identity Centrality Predicts Less Mutability

Study 1 Only

for(i in 1:length(networkMeasures)){
  corsDf <- indDiff1 %>% select(SE:CESD, networkMeasures[i] ) %>% corToOne(., networkMeasures[i])
  barsDf <- indDiff1 %>% select(SE:CESD, networkMeasures[i] ) %>% plotCorToOne(., networkMeasures[i])
  
  assign(paste0(networkMeasures[i],".CorDf"),corsDf)
  assign(paste0(networkMeasures[i],".CorPlotDf"),barsDf)
  
  print(corsDf)
  print(barsDf)
}

Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'


Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'

Positivity of Identity Associated with Greater Intergroup Bias

Study 2 Only

More positive self-reported

m<-lmer(scale(interG) ~ scale(pos) + ( scale(pos) | subID) + ( 1  | id), data=idShort2)
summary(m)

More positive traits associated with identity than negative

m<-lmer(scale(interG) ~ scale(pndiff) + ( scale(pndiff) | subID) + ( 1  | id), data=idShort2)
summary(m)

Differentiation and Inclusion Quadratic Association

Study 2 Only

m<-lmer(scale(differ) ~ scale(poly(inclus, 2)) + ( scale(inclus) | subID) + ( 1  | id), data=idShort2[!is.na(idShort2$differ) & !is.na(idShort2$inclus), ])
summary(m)
ggpredict(m, c(  "inclus" )) %>% plot(show.title=FALSE)

Differentiation and Inclusion Interact in Predicting Identification

Study 2 Only

m<-lmer(scale(streng) ~ scale(inclus) * scale(differ) + ( scale(inclus) + scale(differ) | subID) + ( 1  | id), data=idShort2)
summary(m)
ggpredict(m, c(  "inclus" , "differ")) %>% plot(show.title=FALSE)
m<-lmer(scale(streng) ~ scale(poly(inclus, 2)) + scale(poly(differ, 2)) + ( scale(poly(inclus, 2)) + scale(poly(differ, 2)) | subID) + ( 1  | id), data=idShort2[!is.na(idShort2$differ) & !is.na(idShort2$inclus), ])
summary(m)
ggpredict(m, c(  "inclus[all]" , "differ[all]")) %>% plot(show.title=FALSE)

Network Measures Correlated with External Measures

attempted in tidyverse

# networkMeasures %>% map(~ .x) %>% select(indDiff1, SE:CESD, .)
# 
# selected<-map(networkMeasures, ~ select(indDiff1, SE:CESD, .) )
# map2(networkMeasures, ~ selected, networkMeasures )

Study 1

networkMeasures <- indDiff1 %>% select(matches("H_index"):matches("globEff")) %>% colnames(.)

for(i in 1:length(networkMeasures)){
  corsDf <- indDiff1 %>% select(SE:CESD, networkMeasures[i] ) %>% corToOne(., networkMeasures[i])
  barsDf <- indDiff1 %>% select(SE:CESD, networkMeasures[i] ) %>% plotCorToOne(., networkMeasures[i])
  
  assign(paste0(networkMeasures[i],".CorDf"),corsDf)
  assign(paste0(networkMeasures[i],".CorPlotDf"),barsDf)
  
  print(corsDf)
  print(barsDf)
}
[1] "All required packages attached"

Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'
[1] "All required packages attached"

Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'
[1] "All required packages attached"

Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'
[1] "All required packages attached"

Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'
[1] "All required packages attached"

Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'
[1] "All required packages attached"

Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'
[1] "All required packages attached"

Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'
[1] "All required packages attached"

Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'
[1] "All required packages attached"

Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'
[1] "All required packages attached"

Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'
[1] "All required packages attached"

Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'
[1] "All required packages attached"

Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'
[1] "All required packages attached"

Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'
[1] "All required packages attached"

Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'
[1] "All required packages attached"

Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'
[1] "All required packages attached"

Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'
[1] "All required packages attached"

Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'
[1] "All required packages attached"

Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'
[1] "All required packages attached"

Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'
[1] "All required packages attached"

Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'
[1] "All required packages attached"

Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'
[1] "All required packages attached"

Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'
[1] "All required packages attached"

Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'
[1] "All required packages attached"

Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'
[1] "All required packages attached"

Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'
[1] "All required packages attached"

Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'
[1] "All required packages attached"

Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'
[1] "All required packages attached"

Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'

Study 2

networkMeasures <- indDiff2 %>% select(matches("H_index"):matches("globEff")) %>% colnames(.)

for(i in 1:length(networkMeasures)){
  corsDf <- indDiff2 %>% select(SE:MC, networkMeasures[i] ) %>% corToOne(., networkMeasures[i])
  barsDf <- indDiff2 %>% select(SE:MC, networkMeasures[i] ) %>% plotCorToOne(., networkMeasures[i])
  
  assign(paste0(networkMeasures[i],".CorDf"),corsDf)
  assign(paste0(networkMeasures[i],".CorPlotDf"),barsDf)
  
  print(corsDf)
  print(barsDf)
}

Normative Latino Ratings

normTraits <- read.csv("~/Google Drive/Volumes/Research Project/Identities to Traits/Study 2/Cleaning/Output/Normative/normativeDfStudy12.csv", header = T)

fullLong1s <- fullLong1 %>% select(subID,id,idC,connect,traits,selfResp,IdIn,connect,streng)
fullLong2s <- fullLong2 %>% select(subID,id,idC,connect,traits,selfResp,IdIn,connect,streng)
fullLong2s$subID <- fullLong2$subID + 100000

combLong <- rbind(fullLong1s,fullLong2s)

combLong <- merge(combLong, normTraits, by = "traits")
combLong$ynLatin <- ifelse(combLong$idC==2 & combLong$id=="Race", "HL", "Not HL")
combLong$ynAsian <- ifelse(combLong$idC==4 & combLong$id=="Race", "As", "Not As")
combLong$ynMale <- ifelse(combLong$idC==1 & combLong$id=="Gen", "M", "Not M")
combLong$ynFemale <- ifelse(combLong$idC==2 & combLong$id=="Gen", "F", "Not F")
combLong$ynHetero <- ifelse(combLong$idC==1 & combLong$id=="Sex", "Het", "Not Het")
combLong$ynBis <- ifelse(combLong$idC==3 & combLong$id=="Sex", "Bi", "Not Bi")
combLong$ynCath <- ifelse(combLong$idC==1 & combLong$id=="Rel", "Cath", "Not Cath")
combLong$ynChrist <- ifelse(combLong$idC==2 & combLong$id=="Rel", "Christ", "Not Christ")
combLong$ynAgnos <- ifelse(combLong$idC==8 & combLong$id=="Rel", "Agn", "Not Agn")
combLong$ynAthei <- ifelse(combLong$idC==9 & combLong$id=="Rel", "Ath", "Not Ath")
combLong$ynDem <- ifelse(combLong$idC==1 & combLong$id=="Pol", "Dem", "Not Dem")

m <- lmer( scale(selfResp) ~ scale(Female)*ynFemale*streng + ( scale(Female) | subID) + ( 1 | traits), data = GenLong)
Error in eval(predvars, data, env) : object 'streng' not found
---
title: "R Notebook"
output: html_notebook
---

```{r}
library(ggeffects)
library(dplyr)
library(ggpubr)
library(grid)
library(lmerTest)
```

```{r}
library(devtools)
source_url("https://raw.githubusercontent.com/JacobElder/MiscellaneousR/master/corToOne.R")
source_url("https://raw.githubusercontent.com/JacobElder/MiscellaneousR/master/plotCommAxes.R")
```

```{r}
fullLong1 <- read.csv("~/Google Drive/Volumes/Research Project/Identities to Traits/Study 1/Cleaning/Output/id2traitDf.csv", header = T)
orderDf1 <- read.csv( "~/Google Drive/Volumes/Research Project/Identities to Traits/Study 1/Cleaning/Output/orderDf.csv", header = T)
idShort1 <- read.csv( "~/Google Drive/Volumes/Research Project/Identities to Traits/Study 1/Cleaning/Output/id2traitShort.csv", header = T)
indDiff1 <- read.csv( "~/Google Drive/Volumes/Research Project/Identities to Traits/Study 1/Cleaning/Output/indDiff.csv", header = T)
idSim1 <- read.csv( "~/Google Drive/Volumes/Research Project/Identities to Traits/Study 1/Cleaning/Output/identitySimDf.csv", header = T)
#idToSim1 <- read.csv("~/Google Drive/Volumes/Research Project/Identities to Traits/Study 1/Cleaning/Output/simDf.csv", header = T)
```

```{r}
fullLong2 <- read.csv("~/Google Drive/Volumes/Research Project/Identities to Traits/Study 2/Cleaning/Output/id2traitDf.csv", header = T)
orderDf2 <- read.csv( "~/Google Drive/Volumes/Research Project/Identities to Traits/Study 2/Cleaning/Output/orderDf.csv", header = T)
idShort2 <- read.csv( "~/Google Drive/Volumes/Research Project/Identities to Traits/Study 2/Cleaning/Output/id2traitShort.csv", header = T)
indDiff2 <- read.csv( "~/Google Drive/Volumes/Research Project/Identities to Traits/Study 2/Cleaning/Output/indDiff.csv", header = T)
idSim2 <- read.csv( "~/Google Drive/Volumes/Research Project/Identities to Traits/Study 2/Cleaning/Output/identitySimDf.csv", header = T)
#idToSim2 <- read.csv("~/Google Drive/Volumes/Research Project/Identities to Traits/Study 2/Cleaning/Output/simDf.csv", header = T)
```

```{r}
# subset data for traits to only appear once per subject

traitsPerS1 <- fullLong1 %>% distinct(subID, Idx, .keep_all = TRUE)
traitsPerS2 <- fullLong2 %>% distinct(subID, Idx, .keep_all = TRUE)

# subset data for only connected traits to appear per subject

connectDf1 <- fullLong1 %>% filter(connect==1)
connectDf2 <- fullLong2 %>% filter(connect==1)

# convert to factors

fullLong1$connect <- as.factor(fullLong1$connect)
levels(fullLong1$connect) <- list(No  = "0", Yes = "1")

fullLong2$connect <- as.factor(fullLong2$connect)
levels(fullLong2$connect) <- list(No  = "0", Yes = "1")

# pos neg asymmetry

idShort1$pndiff <- idShort1$pI2Tdeg - idShort1$nI2Tdeg
idShort2$pndiff <- idShort2$pI2Tdeg - idShort2$nI2Tdeg
```

# Identity Typicality

Traits that are nominated as typical of some identity are evaluated more self-descriptively

## Study 1

```{r}
connect1 <- lmer(scale(selfResp) ~ connect + scale(subTend) + scale(traitTend) + ( connect | subID ) + ( 1 | traits), data=fullLong1)
summary(connect1)
connect1.plot <- ggpredict(connect1, c("connect")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Identity-Typicality") + ylab("Self-Evaluation")
connect1.plot
```


## Study 2

```{r}
connect2 <- lmer(scale(selfResp) ~ connect + subTend + traitTend + ( connect | subID ) + ( 1 | traits), data=fullLong2)
summary(connect2)
connect2.plot <- ggpredict(connect2, c("connect")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Identity-Typicality") + ylab("Self-Evaluation")
connect2.plot
```

## Combined

```{r}
plotCommAxes(connect1.plot, connect2.plot, "Connect", "Self-Evaluation")
```

# Effect of Identity Typicality Depends on Identity Importance

## Study 1

Identity importance defined by strength of identification. This is not significant for identity-to-identity centrality.

```{r}
connect.streng1 <- lmer(scale(selfResp) ~ connect * scale(streng) + subTend + traitTend + ( connect + scale(streng) | subID ) + ( 1 | traits), data=fullLong1)
summary(connect.streng1)
connect.streng1.plot <- ggpredict(connect.streng1, c("streng","connect")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Strength of Identification") + ylab("Self-Evaluation")
connect.streng1.plot
```

## Study 2

```{r}
connect.streng2 <- lmer(scale(selfResp) ~ connect * scale(streng) + ( connect + scale(streng) | subID ) + ( 1 | traits), data=fullLong2)
summary(connect.streng2)
connect.streng2.plot <- ggpredict(connect.streng2, c("streng","connect")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Strength of Identification") + ylab("Self-Evaluation")
connect.streng2.plot
```

## Combined

```{r}
plotCommAxes(connect.streng1.plot, connect.streng2.plot, "Strength of Identification", "Self-Evaluation")
```

# Effect of Identity Typicality Depends on Size

## Size Difference

```{r}
connect.size2 <- lmer(scale(selfResp) ~ connect * scale(sizeD) + ( connect + scale(sizeD) | subID ) + ( 1 | traits), data=fullLong2)
summary(connect.size2)
connect.size2.plot <- ggpredict(connect.size2, c("sizeD","connect")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Size Differences") + ylab("Self-Evaluation")
connect.size2.plot
```

# Identity Connections

Traits that are nominated as typical of more identities are evaluated more self-descriptively

## Study 1

```{r}
moconn1 <- lmer(scale(selfResp) ~ scale(IdIn) + ( scale(IdIn) | subID ) + ( 1 | traits), data=traitsPerS1)
summary(moconn1)
moconn1.plot <- ggpredict(moconn1, c("IdIn")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Identity-Associations") + ylab("Self-Evaluation")
moconn1.plot
```


## Study 2

```{r}
moconn2 <- lmer(scale(selfResp) ~ scale(IdIn) + ( scale(IdIn) | subID ) + ( 1 | traits), data=traitsPerS1)
summary(moconn2)
moconn2.plot <- ggpredict(moconn2, c("IdIn")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Identity-Associations") + ylab("Self-Evaluation")
moconn2.plot
```

## Combined

```{r}
plotCommAxes(moconn1.plot, moconn2.plot, "Identity-Typicality", "Self-Evaluation")
```

# Identity Overlap

## Study 1

```{r}
sm1<-lmer(scale(selfResp) ~ scale(T.Sim) * scale(streng) + scale(subTend) + scale(traitTend)   + ( scale(order) | subID ) + ( 1 | traits), data=orderDf1, control=lmerControl(optimizer="bobyqa",
                                 optCtrl=list(maxfun=2e5)))
summary(sm1)
sm1.plot <- ggpredict(sm1, c("T.Sim", "streng")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Overlap with Identity") + ylab("Self-Evaluation")
sm1.plot
```

## Study 2

```{r}
sm2<-lmer(scale(selfResp) ~ scale(T.Sim) * scale(streng) + scale(subTend) + scale(traitTend)  + ( scale(order) | subID ) + ( 1 | traits), data=orderDf2, control=lmerControl(optimizer="bobyqa",
                                 optCtrl=list(maxfun=2e5)))
summary(sm2)
sm2.plot <- ggpredict(sm2, c("T.Sim", "streng")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Overlap with Identity") + ylab("Self-Evaluation")
sm2.plot
```

## Combined Plot

```{r}
plotCommAxes(sm1.plot, sm2.plot, "Identity Overlap", "Self-Evaluation")
```

# Identity Distance

## Study 1

```{r}
dm1<-lmer(scale(selfResp) ~ scale(order) * scale(streng) + scale(subTend) + scale(traitTend)  + ( scale(order) | subID ) + ( 1 | traits), data=orderDf1)
summary(dm1)
dm1.plot <- ggpredict(dm1, c("order", "streng")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Distance from Identity") + ylab("Self-Evaluation")
dm1.plot
```

## Study 2

```{r}
dm2<-lmer(scale(selfResp) ~ scale(order) * scale(streng) + scale(subTend) + scale(traitTend)  + ( scale(order) | subID ) + ( 1 | traits), data=orderDf2)
summary(dm2)
dm2.plot <- ggpredict(dm2, c("order", "streng")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Distance from Identity") + ylab("Self-Evaluation")
dm2.plot
```

## Combined Plot

```{r}
plotCommAxes(dm1.plot, dm2.plot, "Identity Distance", "Self-Evaluation")
```

# Identity Similarity and Distance Joint Influence on Self-Evaluations (Principal Component)

## Study 1

```{r}
pca1<-lmer(scale(selfResp) ~ scale(PCAdist) * scale(streng) + scale(subTend) + scale(traitTend) + ( scale(PCAdist) | subID ) + ( 1 | traits), data=orderDf1)
summary(pca1)
pca1.plot <- ggpredict(pca1, c("PCAdist", "streng")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Composite Identity-Overlap") + ylab("Self-Evaluation")
pca1.plot
```

## Study 2

```{r}
pca2<-lmer(scale(selfResp) ~ scale(PCAdist) * scale(streng) + scale(subTend) + scale(traitTend) + ( scale(PCAdist) | subID ) + ( 1 | traits), data=orderDf2)
summary(pca2)
pca2.plot <- ggpredict(pca2, c("PCAdist", "streng")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Composite Identity-Overlap") + ylab("Self-Evaluation")
pca2.plot
```

## Combined Plot

```{r}
plotCommAxes(pca1.plot, pca2.plot, "Composite Identity-Overlap", "Self-Evaluation")
```

# Identity Centrality Predicts Strength of Identification

## Study 1

```{r}
I2I1.streng<-lmer(scale(streng) ~ scale(I2Ideg) + ( scale(I2Ideg) | subID ) + ( 1 | id), data=idShort1)
summary(I2I1.streng)
I2I1.streng.plot <- ggpredict(I2I1.streng, c("I2Ideg")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Identity-to-Identity Centrality") + ylab("Self-Evaluation")
I2I1.streng.plot
```

## Study 2

```{r}
I2I2.streng<-lmer(scale(streng) ~ scale(I2Ideg) + ( scale(I2Ideg) | subID ) + ( 1 | id), data=idShort2)
summary(I2I2.streng)
I2I2.streng.plot <- ggpredict(I2I2.streng, c("I2Ideg")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Identity-to-Identity Centrality") + ylab("Self-Evaluation")
I2I2.streng.plot
```

## Combined Plot

```{r}
plotCommAxes(I2I1.streng.plot, I2I2.streng.plot, "Identity-to-Identity Centrality", "Strength of Identification")
```

# Strength of Identification Predicts More Shared Traits Between Identities

## Study 1

```{r}
TSharedStreng1 <- lmer( scale(traitCommNod) ~ scale(streng) + ( scale(streng)  | subID) + ( 1 | id), data=idShort1)
summary(TSharedStreng1)
TSharedStreng1.plot <- ggpredict(TSharedStreng1, c("streng")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Strength of Identification") + ylab("Shared Traits")
```

## Study 2

```{r}
TSharedStreng2 <- lmer( scale(traitCommNod) ~ scale(streng) + ( scale(streng)  | subID) + ( 1 | id), data=idShort2)
summary(TSharedStreng2)
TSharedStreng2.plot <- ggpredict(TSharedStreng2, c("streng")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Strength of Identification") + ylab("Shared Traits")
```

## Combined Plot

```{r}
ggpubr::ggarrange(TSharedStreng1.plot, TSharedStreng2.plot)
plotCommAxes(TSharedStreng1.plot, TSharedStreng2.plot, "Strength of Identification", "Proportion of Traits in Common")
```

# Does positvity of identity predict asymmetries in valenced content?

## Study 1

```{r}
asym.pos1 <-lmer(pos ~ pndiff + ( pndiff | subID) + (1 | id), data=idShort1)
summary(asym.pos1)
```

## Study 2

```{r}
asym.pos2 <-lmer(pos ~ pndiff + ( pndiff | subID) + (1 | id), data=idShort2)
summary(asym.pos2)
```

# More trait overlap predicts stronger group identification

## Study 1

```{r}
tcomm.streng1 <-lmer(scale(streng) ~ scale(traitCommNod) + ( scale(traitCommNod) | subID) + (1 | id), data=idShort1)
summary(tcomm.streng1)
```

## Study 2

```{r}
tcomm.streng2 <-lmer(scale(streng) ~ scale(traitCommNod) + ( scale(traitCommNod) | subID) + (1 | id), data=idShort2)
summary(tcomm.streng2)
```

# More identity overlap predicts stronger group identification

## Study 1

```{r}
icomm.streng1 <-lmer(scale(streng) ~ scale(idCommNod) + ( scale(idCommNod) | subID) + (1 | id), data=idShort1)
summary(icomm.streng1)
```

## Study 2

```{r}
icomm.streng2 <-lmer(scale(streng) ~ scale(idCommNod) + ( scale(idCommNod) | subID) + (1 | id), data=idShort2)
summary(icomm.streng2)
```

# Individual Differences Predict Similarity in Identity Judgments

```{r}
m <-lmer(scale(posDist) ~ scale(SE) + ( scale(SE) | subID), data=idSim1)
summary(m)

m <-lmer(scale(strengDist) ~ scale(NFC) + ( scale(NFC) | subID), data=idSim1)
summary(m)

m <-lmer(scale(posDist) ~ scale(DS) + ( 1 | subID), data=idSim1)
summary(m)

m <-lmer(scale(posDist) ~ scale(SCC) + ( 1 | subID), data=idSim1)
summary(m)

m <-lmer(scale(posDist) ~ scale(MemSE) + ( 1 | subID), data=idSim1)
summary(m)

m <-lmer(scale(posDist) ~ scale(PrivCSE) + ( 1 | subID), data=idSim1)
summary(m)

m <-lmer(scale(posDist) ~ scale(PubCSE) + ( 1 | subID), data=idSim1)
summary(m)

m <-lmer(scale(posDist) ~ scale(MemSE) + ( 1 | subID), data=idSim1)
summary(m)
```

# Individual Differences Predict Pairwise Overlap in Traits in Common

## Study 1

### Positive

```{r}
m <-lmer(scale(idtpSim) ~ scale(SCC) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim1)
summary(m)

m <-lmer(scale(idtpSim) ~ scale(Ind) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim1)
summary(m)

m <-lmer(scale(idtpSim) ~ scale(Inter) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim1)
summary(m)

m <-lmer(scale(idtpSim) ~ scale(SWLS) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim1)
summary(m)

m <-lmer(scale(idtpSim) ~ scale(IdImp) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim1)
summary(m)

m <-lmer(scale(idtpSim) ~ scale(phi) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim1)
summary(m)

m <-lmer(scale(idtpSim) ~ scale(overlap_norm) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim1)
summary(m)

m <-lmer(scale(idtpSim) ~ scale(H_index) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim1)
summary(m)

m <-lmer(scale(idtpSim) ~ scale(SE) + scale(idtnSim) + ( scale(idtnSim)| subID), data=idSim1)
summary(m)

m <-lmer(scale(idtpSim) ~ scale(NFC) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim1)
summary(m)

m <-lmer(scale(idtpSim) ~ scale(DS) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim1)
summary(m)

m <-lmer(scale(idtpSim) ~ scale(SCC) + scale(idtnSim) + ( scale(idtnSim)| subID), data=idSim1)
summary(m)

m <-lmer(scale(idtpSim) ~ scale(MemSE) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim1)
summary(m)

m <-lmer(scale(idtpSim) ~ scale(PrivCSE) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim1)
summary(m)

m <-lmer(scale(idtpSim) ~ scale(PubCSE) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim1)
summary(m)

m <-lmer(scale(idtpSim) ~ scale(MemSE) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim1)
summary(m)
```

### Negative

```{r}
m <-lmer(scale(idtnSim) ~ scale(SCC) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim1)
summary(m)

m <-lmer(scale(idtnSim) ~ scale(Ind) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim1)
summary(m)

m <-lmer(scale(idtnSim) ~ scale(Inter) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim1)
summary(m)

m <-lmer(scale(idtnSim) ~ scale(SWLS) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim1)
summary(m)

m <-lmer(scale(idtnSim) ~ scale(IdImp) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim1)
summary(m)

m <-lmer(scale(idtnSim) ~ scale(phi) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim1)
summary(m)

m <-lmer(scale(idtnSim) ~ scale(overlap_norm) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim1)
summary(m)

m <-lmer(scale(idtnSim) ~ scale(H_index) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim1)
summary(m)

m <-lmer(scale(idtnSim) ~ scale(SE) + scale(idtpSim) + ( scale(idtpSim)| subID), data=idSim1)
summary(m)

m <-lmer(scale(idtnSim) ~ scale(NFC) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim1)
summary(m)

m <-lmer(scale(idtnSim) ~ scale(DS) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim1)
summary(m)

m <-lmer(scale(idtnSim) ~ scale(SCC) + scale(idtpSim) + ( scale(idtpSim)| subID), data=idSim1)
summary(m)

m <-lmer(scale(idtnSim) ~ scale(MemSE) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim1)
summary(m)

m <-lmer(scale(idtnSim) ~ scale(PrivCSE) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim1)
summary(m)

m <-lmer(scale(idtnSim) ~ scale(PubCSE) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim1)
summary(m)

m <-lmer(scale(idtnSim) ~ scale(MemSE) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim1)
summary(m)
```

### Both

```{r}
m <-lmer(scale(idtSim) ~ scale(SCC) + ( 1 | subID), data=idSim1)
summary(m)

m <-lmer(scale(idtSim) ~ scale(Ind) + ( 1 | subID), data=idSim1)
summary(m)

m <-lmer(scale(idtSim) ~ scale(Inter) + ( 1 | subID), data=idSim1)
summary(m)

m <-lmer(scale(idtSim) ~ scale(SWLS) + ( 1 | subID), data=idSim1)
summary(m)

m <-lmer(scale(idtSim) ~ scale(IdImp) + ( 1 | subID), data=idSim1)
summary(m)

m <-lmer(scale(idtSim) ~ scale(phi) + ( 1 | subID), data=idSim1)
summary(m)

m <-lmer(scale(idtSim) ~ scale(overlap_norm) + ( 1 | subID), data=idSim1)
summary(m)

m <-lmer(scale(idtSim) ~ scale(H_index) + ( 1 | subID), data=idSim1)
summary(m)

m <-lmer(scale(idtSim) ~ scale(SE) + ( 1| subID), data=idSim1)
summary(m)

m <-lmer(scale(idtSim) ~ scale(NFC) + ( 1 | subID), data=idSim1)
summary(m)

m <-lmer(scale(idtSim) ~ scale(DS) + ( 1 | subID), data=idSim1)
summary(m)

m <-lmer(scale(idtSim) ~ scale(SCC) + ( 1| subID), data=idSim1)
summary(m)

m <-lmer(scale(idtSim) ~ scale(MemSE) + ( 1 | subID), data=idSim1)
summary(m)

m <-lmer(scale(idtSim) ~ scale(PrivCSE) + ( 1 | subID), data=idSim1)
summary(m)

m <-lmer(scale(idtSim) ~ scale(PubCSE) + ( 1 | subID), data=idSim1)
summary(m)

m <-lmer(scale(idtSim) ~ scale(MemSE) + ( 1 | subID), data=idSim1)
summary(m)
```

# Individual Differences Predict Pairwise Overlap in Identities in Common

```{r}
m <-lmer(scale(idSim) ~ scale(SCC) + ( 1 | subID), data=idSim1)
summary(m)

m <-lmer(scale(idSim) ~ scale(Ind) + ( 1 | subID), data=idSim1)
summary(m)

m <-lmer(scale(idSim) ~ scale(Inter) + ( 1 | subID), data=idSim1)
summary(m)

m <-lmer(scale(idSim) ~ scale(SWLS) + ( 1 | subID), data=idSim1)
summary(m)

m <-lmer(scale(idSim) ~ scale(IdImp) + ( 1 | subID), data=idSim1)
summary(m)

m <-lmer(scale(idSim) ~ scale(phi) + ( 1 | subID), data=idSim1)
summary(m)

m <-lmer(scale(idSim) ~ scale(overlap_norm) + ( 1 | subID), data=idSim1)
summary(m)

m <-lmer(scale(idSim) ~ scale(H_index) + ( 1 | subID), data=idSim1)
summary(m)

m <-lmer(scale(idSim) ~ scale(SE) + ( 1| subID), data=idSim1)
summary(m)

m <-lmer(scale(idSim) ~ scale(NFC) + ( 1 | subID), data=idSim1)
summary(m)

m <-lmer(scale(idSim) ~ scale(DS) + ( 1 | subID), data=idSim1)
summary(m)

m <-lmer(scale(idSim) ~ scale(SCC) + ( 1| subID), data=idSim1)
summary(m)

m <-lmer(scale(idSim) ~ scale(MemSE) + ( 1 | subID), data=idSim1)
summary(m)

m <-lmer(scale(idSim) ~ scale(PrivCSE) + ( 1 | subID), data=idSim1)
summary(m)

m <-lmer(scale(idSim) ~ scale(PubCSE) + ( 1 | subID), data=idSim1)
summary(m)

m <-lmer(scale(idSim) ~ scale(MemSE) + ( 1 | subID), data=idSim1)
summary(m)
```

## Study 2

### Positive

```{r}
m <-lmer(scale(idtpSim) ~ scale(SCC) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim2)
summary(m)

m <-lmer(scale(idtpSim) ~ scale(Ind) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim2)
summary(m)

m <-lmer(scale(idtpSim) ~ scale(Inter) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim2)
summary(m)

m <-lmer(scale(idtpSim) ~ scale(SWLS) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim2)
summary(m)

m <-lmer(scale(idtpSim) ~ scale(IdImp) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim2)
summary(m)

m <-lmer(scale(idtpSim) ~ scale(phi) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim2)
summary(m)

m <-lmer(scale(idtpSim) ~ scale(overlap_norm) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim2)
summary(m)

m <-lmer(scale(idtpSim) ~ scale(H_index) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim2)
summary(m)

m <-lmer(scale(idtpSim) ~ scale(SE) + scale(idtnSim) + ( scale(idtnSim)| subID), data=idSim2)
summary(m)

m <-lmer(scale(idtpSim) ~ scale(NFC) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim2)
summary(m)

m <-lmer(scale(idtpSim) ~ scale(DS) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim2)
summary(m)

m <-lmer(scale(idtpSim) ~ scale(SCC) + scale(idtnSim) + ( scale(idtnSim)| subID), data=idSim2)
summary(m)

m <-lmer(scale(idtpSim) ~ scale(MemSE) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim2)
summary(m)

m <-lmer(scale(idtpSim) ~ scale(PrivCSE) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim2)
summary(m)

m <-lmer(scale(idtpSim) ~ scale(PubCSE) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim2)
summary(m)

m <-lmer(scale(idtpSim) ~ scale(MemSE) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim2)
summary(m)
```

### Negative

```{r}
m <-lmer(scale(idtnSim) ~ scale(SCC) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim2)
summary(m)

m <-lmer(scale(idtnSim) ~ scale(Ind) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim2)
summary(m)

m <-lmer(scale(idtnSim) ~ scale(Inter) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim2)
summary(m)

m <-lmer(scale(idtnSim) ~ scale(SWLS) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim2)
summary(m)

m <-lmer(scale(idtnSim) ~ scale(IdImp) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim2)
summary(m)

m <-lmer(scale(idtnSim) ~ scale(phi) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim2)
summary(m)

m <-lmer(scale(idtnSim) ~ scale(overlap_norm) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim2)
summary(m)

m <-lmer(scale(idtnSim) ~ scale(H_index) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim2)
summary(m)

m <-lmer(scale(idtnSim) ~ scale(SE) + scale(idtpSim) + ( scale(idtpSim)| subID), data=idSim2)
summary(m)

m <-lmer(scale(idtnSim) ~ scale(NFC) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim2)
summary(m)

m <-lmer(scale(idtnSim) ~ scale(DS) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim2)
summary(m)

m <-lmer(scale(idtnSim) ~ scale(SCC) + scale(idtpSim) + ( scale(idtpSim)| subID), data=idSim2)
summary(m)

m <-lmer(scale(idtnSim) ~ scale(MemSE) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim2)
summary(m)

m <-lmer(scale(idtnSim) ~ scale(PrivCSE) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim2)
summary(m)

m <-lmer(scale(idtnSim) ~ scale(PubCSE) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim2)
summary(m)

m <-lmer(scale(idtnSim) ~ scale(MemSE) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim2)
summary(m)
```

### Both

```{r}
m <-lmer(scale(idtSim) ~ scale(SCC) + ( 1 | subID), data=idSim2)
summary(m)

m <-lmer(scale(idtSim) ~ scale(Ind) + ( 1 | subID), data=idSim2)
summary(m)

m <-lmer(scale(idtSim) ~ scale(Inter) + ( 1 | subID), data=idSim2)
summary(m)

m <-lmer(scale(idtSim) ~ scale(SWLS) + ( 1 | subID), data=idSim2)
summary(m)

m <-lmer(scale(idtSim) ~ scale(IdImp) + ( 1 | subID), data=idSim2)
summary(m)

m <-lmer(scale(idtSim) ~ scale(phi) + ( 1 | subID), data=idSim2)
summary(m)

m <-lmer(scale(idtSim) ~ scale(overlap_norm) + ( 1 | subID), data=idSim2)
summary(m)

m <-lmer(scale(idtSim) ~ scale(H_index) + ( 1 | subID), data=idSim2)
summary(m)

m <-lmer(scale(idtSim) ~ scale(SE) + ( 1| subID), data=idSim2)
summary(m)

m <-lmer(scale(idtSim) ~ scale(NFC) + ( 1 | subID), data=idSim2)
summary(m)

m <-lmer(scale(idtSim) ~ scale(DS) + ( 1 | subID), data=idSim2)
summary(m)

m <-lmer(scale(idtSim) ~ scale(SCC) + ( 1| subID), data=idSim2)
summary(m)

m <-lmer(scale(idtSim) ~ scale(MemSE) + ( 1 | subID), data=idSim2)
summary(m)

m <-lmer(scale(idtSim) ~ scale(PrivCSE) + ( 1 | subID), data=idSim2)
summary(m)

m <-lmer(scale(idtSim) ~ scale(PubCSE) + ( 1 | subID), data=idSim2)
summary(m)

m <-lmer(scale(idtSim) ~ scale(MemSE) + ( 1 | subID), data=idSim2)
summary(m)
```

# Individual Differences Predict Pairwise Overlap in Identities in Common

```{r}
m <-lmer(scale(idSim) ~ scale(SCC) + ( 1 | subID), data=idSim2)
summary(m)

m <-lmer(scale(idSim) ~ scale(Ind) + ( 1 | subID), data=idSim2)
summary(m)

m <-lmer(scale(idSim) ~ scale(Inter) + ( 1 | subID), data=idSim2)
summary(m)

m <-lmer(scale(idSim) ~ scale(SWLS) + ( 1 | subID), data=idSim2)
summary(m)

m <-lmer(scale(idSim) ~ scale(IdImp) + ( 1 | subID), data=idSim2)
summary(m)

m <-lmer(scale(idSim) ~ scale(phi) + ( 1 | subID), data=idSim2)
summary(m)

m <-lmer(scale(idSim) ~ scale(overlap_norm) + ( 1 | subID), data=idSim2)
summary(m)

m <-lmer(scale(idSim) ~ scale(H_index) + ( 1 | subID), data=idSim2)
summary(m)

m <-lmer(scale(idSim) ~ scale(SE) + ( 1| subID), data=idSim2)
summary(m)

m <-lmer(scale(idSim) ~ scale(NFC) + ( 1 | subID), data=idSim2)
summary(m)

m <-lmer(scale(idSim) ~ scale(DS) + ( 1 | subID), data=idSim2)
summary(m)

m <-lmer(scale(idSim) ~ scale(SCC) + ( 1| subID), data=idSim2)
summary(m)

m <-lmer(scale(idSim) ~ scale(MemSE) + ( 1 | subID), data=idSim2)
summary(m)

m <-lmer(scale(idSim) ~ scale(PrivCSE) + ( 1 | subID), data=idSim2)
summary(m)

m <-lmer(scale(idSim) ~ scale(PubCSE) + ( 1 | subID), data=idSim2)
summary(m)

m <-lmer(scale(idSim) ~ scale(MemSE) + ( 1 | subID), data=idSim2)
summary(m)
```


# More trait overlap predicts more intergroup bias

## Study 2 Only

```{r}
m<-lmer(scale(interG) ~ scale(traitCommNod) + scale(idCommNod) + ( scale(traitCommNod) + scale(idCommNod) | subID) + ( 1  | id), data=idShort2)
summary(m)
```

# Identity Centrality Predicts Less Mutability

## Study 1 Only

```{r}
mall.I2I<-lmer(scale(mall) ~ scale(I2Ideg) + ( scale(I2Ideg) | subID) + ( 1  | id), data=idShort1)
summary(mall.I2I)
```

# Positivity of Identity Associated with Greater Intergroup Bias

## Study 2 Only

### More positive self-reported

```{r}
m<-lmer(scale(interG) ~ scale(pos) + ( scale(pos) | subID) + ( 1  | id), data=idShort2)
summary(m)
```

### More positive traits associated with identity than negative

```{r}
m<-lmer(scale(interG) ~ scale(pndiff) + ( scale(pndiff) | subID) + ( 1  | id), data=idShort2)
summary(m)
```

# Differentiation and Inclusion Quadratic Association

## Study 2 Only

```{r}
m<-lmer(scale(differ) ~ scale(poly(inclus, 2)) + ( scale(inclus) | subID) + ( 1  | id), data=idShort2[!is.na(idShort2$differ) & !is.na(idShort2$inclus), ])
summary(m)
ggpredict(m, c(  "inclus" )) %>% plot(show.title=FALSE)
```
# Differentiation and Inclusion Interact in Predicting Identification

## Study 2 Only

```{r}
m<-lmer(scale(streng) ~ scale(inclus) * scale(differ) + ( scale(inclus) + scale(differ) | subID) + ( 1  | id), data=idShort2)
summary(m)
ggpredict(m, c(  "inclus" , "differ")) %>% plot(show.title=FALSE)
```

```{r}
m<-lmer(scale(streng) ~ scale(poly(inclus, 2)) + scale(poly(differ, 2)) + ( scale(poly(inclus, 2)) + scale(poly(differ, 2)) | subID) + ( 1  | id), data=idShort2[!is.na(idShort2$differ) & !is.na(idShort2$inclus), ])
summary(m)
ggpredict(m, c(  "inclus[all]" , "differ[all]")) %>% plot(show.title=FALSE)
```

# Network Measures Correlated with External Measures

attempted in tidyverse

```{r}
# networkMeasures %>% map(~ .x) %>% select(indDiff1, SE:CESD, .)
# 
# selected<-map(networkMeasures, ~ select(indDiff1, SE:CESD, .) )
# map2(networkMeasures, ~ selected, networkMeasures )
```


## Study 1

```{r}
networkMeasures <- indDiff1 %>% select(matches("H_index"):matches("globEff")) %>% colnames(.)

for(i in 1:length(networkMeasures)){
  corsDf <- indDiff1 %>% select(SE:CESD, networkMeasures[i] ) %>% corToOne(., networkMeasures[i])
  barsDf <- indDiff1 %>% select(SE:CESD, networkMeasures[i] ) %>% plotCorToOne(., networkMeasures[i])
  
  assign(paste0(networkMeasures[i],".CorDf"),corsDf)
  assign(paste0(networkMeasures[i],".CorPlotDf"),barsDf)
  
  print(corsDf)
  print(barsDf)
}
```

## Study 2

```{r}
networkMeasures <- indDiff2 %>% select(matches("H_index"):matches("globEff")) %>% colnames(.)

for(i in 1:length(networkMeasures)){
  corsDf <- indDiff2 %>% select(SE:MC, networkMeasures[i] ) %>% corToOne(., networkMeasures[i])
  barsDf <- indDiff2 %>% select(SE:MC, networkMeasures[i] ) %>% plotCorToOne(., networkMeasures[i])
  
  assign(paste0(networkMeasures[i],".CorDf"),corsDf)
  assign(paste0(networkMeasures[i],".CorPlotDf"),barsDf)
  
  print(corsDf)
  print(barsDf)
}
```

# Normative Latino Ratings

```{r}
normTraits <- read.csv("~/Google Drive/Volumes/Research Project/Identities to Traits/Study 2/Cleaning/Output/Normative/normativeDfStudy12.csv", header = T)

fullLong1s <- fullLong1 %>% select(subID,id,idC,connect,traits,selfResp,IdIn,connect,streng)
fullLong2s <- fullLong2 %>% select(subID,id,idC,connect,traits,selfResp,IdIn,connect,streng)
fullLong2s$subID <- fullLong2$subID + 100000

combLong <- rbind(fullLong1s,fullLong2s)

combLong <- merge(combLong, normTraits, by = "traits")
combLong$ynLatin <- ifelse(combLong$idC==2 & combLong$id=="Race", "HL", "Not HL")
combLong$ynAsian <- ifelse(combLong$idC==4 & combLong$id=="Race", "As", "Not As")
combLong$ynMale <- ifelse(combLong$idC==1 & combLong$id=="Gen", "M", "Not M")
combLong$ynFemale <- ifelse(combLong$idC==2 & combLong$id=="Gen", "F", "Not F")
combLong$ynHetero <- ifelse(combLong$idC==1 & combLong$id=="Sex", "Het", "Not Het")
combLong$ynBis <- ifelse(combLong$idC==3 & combLong$id=="Sex", "Bi", "Not Bi")
combLong$ynCath <- ifelse(combLong$idC==1 & combLong$id=="Rel", "Cath", "Not Cath")
combLong$ynChrist <- ifelse(combLong$idC==2 & combLong$id=="Rel", "Christ", "Not Christ")
combLong$ynAgnos <- ifelse(combLong$idC==8 & combLong$id=="Rel", "Agn", "Not Agn")
combLong$ynAthei <- ifelse(combLong$idC==9 & combLong$id=="Rel", "Ath", "Not Ath")
combLong$ynDem <- ifelse(combLong$idC==1 & combLong$id=="Pol", "Dem", "Not Dem")
```

```{r}
raceLong <- subset(combLong, id == "Race")
m <- lmer( scale(selfResp) ~ scale(Latino)*ynLatin + ( scale(Latino) | subID) + ( 1 | traits), data = raceLong)
summary(m)
ggpredict(m, c("Latino", "ynLatin")) %>% plot()
```

```{r}
m <- lmer( scale(selfResp) ~ scale(Asian)*ynAsian + ( scale(Asian) | subID) + ( 1 | traits), data = raceLong)
summary(m)
ggpredict(m, c("Asian", "ynAsian")) %>% plot()

m <- lmer( scale(selfResp) ~ scale(Asian)*ynAsian*streng + ( scale(Asian) | subID) + ( 1 | traits), data = raceLong)
summary(m)
ggpredict(m, c("Asian", "streng")) %>% plot()
```

```{r}
GenLong <- subset(combLong, id=="Gen")
m <- lmer( scale(selfResp) ~ scale(Female)*ynFemale + ( scale(Female) | subID) + ( 1 | traits), data = GenLong)
summary(m)
ggpredict(m, c("Female", "ynFemale")) %>% plot()

m <- lmer( scale(selfResp) ~ scale(Female)*ynFemale*streng + ( scale(Female) | subID) + ( 1 | traits), data = GenLong)
summary(m)
ggpredict(m, c("Female", "ynFemale")) %>% plot()
```



